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The Wisdom of the Few
- Lucy Clark
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1 The Wisdom of the Few A Collaborative Filtering Approach Based on Expert Opinions from the Web Xavier Amatriain Telefonica Research Via Augusta, 177 Barcelona 08021, Spain Haewoon Kwak KAIST Computer Science Dept. Kuseong-dong, Yuseong-gu Daejeon , Korea Neal Lathia Dept. of Computer Science University College of London Gower Street London WC1E 6BT, UK Nuria Oliver Telefonica Research Via Augusta, 177 Barcelona 08021, Spain Josep M. Pujol Telefonica Research Via Augusta, 177 Barcelona 08021, Spain ABSTRACT Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Information Filtering General Terms Algorithms, Performance, Theory Keywords Recommender Systems, Collaborative Filtering, Experts, Cosine Similarity, Nearest Neighbors, Top-N Recommendations Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 200X ACM X-XXXXX-XX-X/XX/XX...$ INTRODUCTION Collaborative filtering (CF) is the current mainstream approach used to build web-based recommender systems [1]. CF algorithms assume that in order to recommend items to users, information can be drawn from what other similar users liked in the past. The Nearest Neighbor algorithm, for instance, does so by finding, for each user, a number of similar users whose profiles can then be used to predict recommendations. However, defining similarity between users is not an easy task: it is limited by the sparsity and noise in the data and is computationally expensive. In this work, we explore how professional raters in a given domain (i.e. experts) can predict the behavior of the general population. In recent work [2], we have found that a significant part of the error in explicit feedback-based CF algorithms is due to the noise in the users explicit feedback. Therefore, we aim at using feedback from less noisy sources (i.e. experts in the context of this work) to build recommendations. We define an expert as an individual that we can trust to have produced thoughtful, consistent and reliable evaluations (ratings) of items in a given domain. Our goal is not to increase CF accuracy, but rather to: (a) study how preferences of a large population can be predicted by using a very small set of users; (b) understand the potential of an independent and uncorrelated data set to generate recommendations; (c) analyze whether professional raters are good predictors for general users; and (d) discuss how this approach addresses some of the traditional pitfalls in CF. The contributions of this paper include: 1. Collecting and comparing, in Section 2, the characteristics of two datasets: the Netflix dataset 1 of usermovie ratings, and the opinions collected from the web from over 150 movie critics (experts). 2. Designing an approach to predict personalized user ratings from the opinions of the experts. Section 3 outlines traditional CF and describes the proposed algorithm. 1
2 3. Evaluating the use of expert opinions as predictors of user preferences, both in terms of prediction accuracy and recommendation list precision (described in Section 4). In Section 5 we complement these results with a user study where we compare our approach with three baseline methods: random, standard Nearest- Neighbor CF and average popularity in the experts data set. 2. MINING THE WEB FOR EXPERT RATINGS The first step in our approach requires obtaining a set of ratings from a reduced population of experts in a given domain. One option is to obtain item evaluations from trusted sources and use a rating inference model [3] or an automatic expert detection model [4]. However, in domains where there are online expert evaluations (e.g. movies, books, cars, etc.) that include a quantitative rating, it is feasible to crawl the web in order to gather expert ratings. In our work, we have crawled the Rotten Tomatoes 2 web site which aggregates the opinions of movie critics from various media sources, to obtain expert ratings of the movies in the Netflix data set. Note that there are other approaches to populate a database of expert ratings, ranging from a manuallymaintained database of dedicated experts to the result of crawling and inferring quantitative ratings from online reviews. The focus of our work is not on extracting the expert ratings, but on using such an external and reduced source of ratings to predict the general population. The ratings extracted from our experts source correspond to 8, 000 of the total of 17, 770 movies in the Netflix data set. The missing movies had significantly different titles in both databases and were difficult to match. For the purpose of this study, we believe that 50% of the Netflix data set movies is a sufficiently large sample. We collected the opinions of 1, 750 experts. However, an initial analysis showed that many of them had very few ratings and were therefore not adding any improvement to our predictions (see per user distribution in the experts data set in Figure 2b). Therefore, we removed those experts who did not contain at least ρ ratings of the Netflix movies. Using a final threshold of ρ = 250 minimum ratings, we kept 169 experts. The relation between ρ and the number of selected experts is depicted in Figure 1. This low number of experts number highlights the potential of our method to predict user preferences using a small population as the source. 2.1 Dataset analysis: Users and Experts Before reporting the results on the performance of our expert-cf approach, we compare next the expert and Netflix datasets. Number of Ratings and Data Sparsity. The sparsity coefficient of the user data set is roughly 0.01, meaning that only 1% of the positions in the user matrix have non-zero values. Figure 2b depicts the distribution of the number of ratings per user and per expert. An average Netflix user has rated less than 100 movies while only 10% have rated over 400 movies. Conversely, the expert set contains around 100, 000 movie ratings, yielding a sparsity coefficient of Figure 2b also shows that an average expert has rated around 400 movies and 10% have rated 1, 000 movies 2 Figure 1: Relation between minimum ratings threshold and number of selected experts (a) (b) Figure 2: Comparison of the CDF of ratings per (a) movie and (b) user in Netflix and Rotten Tomatoes (experts) Datasets or more. Similarly, Figure 2a depicts the distribution of the number of ratings per movie: the average movie has over 1, 000 Netflix user ratings, compared to an average of 100 expert ratings. Note that 20% of the movies in our expert data set only have one rating 3. However, the expert matrix is less sparse than the user matrix and more evenly distributed, both per user and per movie. Average Rating Distribution. Figures 3a and 3b depict the distribution of the mean score of the ratings per movie (a) and user (b) in the Netflix (red line) and Rotten Tomatoes or expert (green line) datasets. As seen in Figure 3a, the average rating in Netflix is around 0.55 (or 3.2 ); 10% of the movies have a rating of 0.45 (2.8 ) or less, while 10% of the movies have an average rating of 0.7 (4 ) or higher. Conversely, experts rate movies with an average score slightly larger than 0.6 (3.5 ); 10% of the movies have 3 This was not a result of our filtering of experts with few ratings, but a limitation of our original data set (a) (b) Figure 3: Average rating per movie (a) and user (b) in Netflix and Rotten Tomatoes
3 Figure 4: Per movie (a) and user (b) standard deviation (std) in Netflix and Rotten Tomatoes a mean rating 0.4 (2 ), but the range 0.8 to 1 also accounts for 10% of the movies. In a similar way, Figure 3b shows that the user ratings have a normal distribution centered around 0.7 (4 ) while expert ratings are centered around 0.6 (3.5 ). Note that in this case, only 10% of the users have a mean score 0.55 and another 10% 0.7. In terms of how the average rating is distributed, we see that experts show greater variability per movie than per user: while experts tend to behave similarly in terms of their average rating, their overall opinion on the movies is more varied. This could be due to underlying incentives to rate movies, since experts are likely to watch and rate movies regardless of whether they like them or not, while users tend to be biased towards positively rating movies [5]. We also detect a much larger proportion of movies in the highest rating range for experts. Experts seem to consistently agree on what the excellent movies are. Rating Standard Deviation (std). Figures 4a and 4b plot the distribution of the std per movie (a) and user (a) in Netflix and Rotten Tomatoes, respectively. In the case of users, the std per movie (Figure 4a) is centered around 0.25 (1 ) with very little variation, while the expert data set has significantly lower std (0.15) and larger variation. Note that in the expert data, 20% of the items show no std as there is only one rating. The std per user (Figure 4b) is centered around 0.25 for the Netflix data with larger variability than in the per movie case. When looking at the expert data, the average std per user is 0.2 with small variability. The above analysis highlights the large differences between our user and expert sets. The expert data set is much less sparse than the users. Experts rate movies all over the rating scale instead of being biased towards rating only popular movies. However, they seem to consistently agree on the good movies. Experts also have a lower overall standard deviation per movie: they tend to agree more than regular users. Also, the per-expert standard deviation is lower than that seen between users, meaning that they tend to deviate less from their personal average rating. 3. EXPERT NEAREST-NEIGHBORS The generic CF method applies the knn algorithm to predict user ratings. The algorithm computes a prediction for a user-item pair, based on a number k of nearest neighbors, which can either be user- or item-based [6]. Although it is possible to use either approach, we choose user-based CF for its transparent applicability to experts. Both approaches can be decomposed into a sequence of stages. In the first stage, a matrix of user-item ratings is populated. Then, the similarity between all pairs of users is computed, based on a pre-determined measure of similarity. In a similar way to what Herlocker et. al propose [7], we use a variation of the cosine similarity which includes an adjusting factor to take into account the number of items co-rated by both users. Given users a and b, item i, user-item ratings r ai and r bi, the number of items N a and N b rated by each user, and the number of co-rated items N a b the similarity is computed as: sim(a,b) = P i (r air bi ) q Pi q Pi rai 2 rbi 2 2N a b N a + N b (1) We propose an approach to CF that only uses expert opinions to predict user ratings. Therefore, our approach does not require the user-user similarity to be computed; instead, we build a similarity matrix between each user and the expert set. We take a slightly different approach than regular k-nn CF. Our expert-cf method is closely related to Ma et al. s method for neighborhood selection [8]. In order to predict a user s rating for a particular item, we look for the experts whose similarity to the given user is greater than δ. Formally: given a space V of users and experts and a similarity measure sim: V V R, we define a set of experts E = {e 1,..., e k } V and a set of users U = {u 1,..., u N } V. Given a particular user u U and a value δ, we find the set of experts E E such that: e E sim(u, e) δ. One of the drawbacks of using a fixed-threshold δ is the risk of finding very few neighbors; furthermore, the ones that are found may not have rated the current item. In order to deal with this problem, we define a confidence threshold τ as the minimum number of expert neighbors who must have rated the item in order to trust their prediction. Given the set of experts E found in the previous step and an item i, we find the subset E E such that e E r ei, where r ei is the rating of item i by expert e E, and is the value of the unrated item. Once this subset of experts E = e 1...e n has been identified, if n < τ, no prediction can be made and the user mean is returned. On the other hand, if n τ, a predicted rating can be computed. This is done by means of a similarityweighted average of the ratings input from each expert e in E [9]: P e E (r r ai = σ u + ei σ e)sim(e,a) P (2) sim(e, a) where r ai is the predicted rating of item i for user a, r ei is the known rating for expert e to item i, and σ u and σ e are the respective mean ratings. In the next section, we report on the interplay of the two parameters δ and τ. The optimal setting of these parameters depends on the data set and the application in mind, as it is the case with other state-of-the-art CF algorithms. Also, and in order to make our results comparable, note that we use the same threshold-based nearest-neighbor approach when comparing with the standard CF method. 4. RESULTS Based on the previously described data, we measure how well the 169 experts predict the ratings of the 10, 000 Netflix users. In order to validate our approach, we set up two different experiments: in the first experiment, we measure the mean error and coverage of the predicted recommendations. In the second experiment, we measure the precision of the
4 Method MAE Coverage Critics Choice % Expert-CF % N eighbor-cf % Table 1: Summary of the MAE and Coverage in our Expertbased CF approach compared to Critics Choice and Neighbor CF recommendation lists generated for the users. 4.1 Error in Predicted Recommendations In order to evaluate the predictive potential of expert ratings, we divided our user data set (by random sampling) into 80% training - 20% testing sets and report the average results of a 5-fold cross-validation. We use the average for all experts over each given item as a worst-case baseline measure. This is equivalent to a non-personalized critics choice recommendation, which produces a Mean Average Error (MAE) of and full coverage. Setting our parameters to τ = 10 and δ = 0.01, we obtain a MAE of and a coverage of 97.7%. Therefore, expert-cf yields a significant accuracy improvement with respect to using the experts average. As far as coverage is concerned, the setting of the parameters represents a small loss. We shall turn next to the details of how the two parameters in our approach (δ or minimum similarity and τ or confidence threshold) interplay. Figure 5a shows that the MAE in the expert-cf approach is inversely proportional to the similarity threshold (δ) until the 0.06 mark, when it starts to increase as we move to higher δ values. The accuracy below the 0.0 threshold degrades rapidly 4, as we are taking into account too many experts; above 0.06, though, we have too few experts in the neighborhood to make a good prediction. If we look at Figure 5b we see how it decreases as we increase δ. For the optimal MAE point of 0.06, coverage is still above 70%. The ultimate trade-off between MAE and coverage will be application-specific. Turning to Figure 5c, we see how the MAE evolves as a function of the confidence threshold (τ). The two depicted curves correspond to δ = 0.0 and δ = We choose these two values of our similarity threshold as they produce a reasonable tradeoff between accuracy and coverage. Standard neighbor-cf (ignoring the expert data set) offers a second baseline measure of performance. A side-toside comparison gives us a first intuition. Using the Netflix users as neighbors, we measure a MAE of and 92.9% coverage when the δ = 0.01 and τ = 10 (see summary in Table 1) 5. Figure 6 includes a detailed comparison of the accuracy and coverage of the expert-cf (experts line) and NN-CF (neighbors) methods as a function of the similarity threshold. While NN-CF has a MAE 10% lower than expert-cf, the difference in their coverage is also around 10%, favoring the experts in this case. 4 Note that this threshold values are dependent on the chosen similarity measure. In our case, we are using a symmetric cosine similarity that can yield values between [ 1,+1]. 5 It should be noted, for consistency sake, that using this parameters yields very similar results to standard knn CF with k = 50, which is a common setting [10]. In this case we measure a MAE of Finally, we are interested in measuring whether the difference in prediction accuracy is equally distributed among all target users. Figure 6b includes the cumulative distribution of per-user error for both methods. Note how both curves separate at around MAE = 0.5 and they run almost parallel with a separation of around 0.1 until they start joining again at around the point of MAE = 1. This means that neighbor NN works better for the minority of users with a low MAE average of less than 0.5, which represent around 10% of our population. Both methods perform equally the same for the rest of the users with the expert-cf approach performing even slightly better for users with a MAE higher than one. This is a positive feature of our approach that only performs worse on users that are highly predictable, in which a slight increase in error should be acceptable. 4.2 Top-N Recommendation Precision Although measuring the mean error on all predictions for a test data set is currently an accepted measure of success for recommender systems, we are interested in evaluating our results in a more realistic setting. A real-world recommender system only recommends the set of items that the user may like. This is similar to the idea of top-n recommendation lists [11]. The evaluation of recommendation algorithms through top-n measures has been addressed before [12, 13]. All of these approaches rely on the use of the well-known precision and recall measures. Ziegler et al. show [14] that evaluating recommender algorithms through top-n lists measures does not map directly to the user s utility function. However, it does address some of the limitations of the more commonly accepted accuracy measures, such as MAE. We propose a variation as an extension to the previously described approaches. In our case, we do not construct the list of recommendable items by fixing N, but rather classify items as being recommendable or not recommendable given a threshold: if there is no item in the test set that is worth recommending to a given target user, we simply return an empty list. We believe that using a recommendable threshold is a more appropriate measure for top-n recommendations than, for instance, the ones proposed by Deshpande and Karipis [11], where the user rating was not taken into account in the evaluation process. The most similar approach to ours is that proposed by Basu et al. [15], where they use the top quartile of a user s ratings in order to decide whether a movie is recommendable. We do not use the modified precision measure proposed in [13] for two reasons: First, because we believe it is unfair with algorithms that promote serendipity. And second, and most important, because we will later do a final validation with a user study in which we will use the same procedure for generating the lists. In that setting, we will aim at recommending previously unrated items. Therefore, penalizing unrated items as proposed in the modified precision would make both results not comparable. Therefore, we define an item to be recommendable if its predicted value is greater than σ. With this definition in mind, we evaluate our system as a 2-class classification problem: 1. For a given user, compute all predictions and present those greater or equal than σ to the user 2. For all predicted items that are present in the user s
5 (a) MAE vs. similarity threshold (b) Coverage vs. similarity threshold (c) MAE vs. Confidence Figure 5: (a) Mean absolute error (MAE) and (b) coverage of expert-predicted ratings as a function of the minimum similarity (δ) and confidence (τ) thresholds; (c) MAE versus confidence threshold. Effect of Similarity Threshold User Error Distribution Mean Absolute Error experts neighbors Cumulative Distribution neighbors experts Similarity Threshold User Mean Absolute Error (a) Figure 6: Comparison between Expert CF and Nearest-Neighbor CF: (a) MAE and (b) per user error. (b) test set, look at whether it is a true positive (actual user rating greater or equal to σ) or a false positive (actual user rating less than σ). 3. Compute the precision of our classifications using the classical definition of this measure We measure a precision of 0.76 using the same parameter values reported in Section 4.1 and setting σ = 4. This means that 76% of the items recommended by the experts found in the user test set are qualified as recommendable by the users. Note, however, that a recommendable threshold of 4 is quite restrictive. If we lower σ to 3, we measure a precision of 89%. Figure 4.2 depicts the variation of the precision of NN-CF (neighbors line) and expert-cf (experts line) with respect to σ. For σ = 4, the baseline method clearly outperforms our expert-based approach with a precision of However, for σ = 3 the precision in both methods is similar. Therefore, for users willing to accept recommendations for any above average item, the expert-based method appears to behave as well as a standard NN-CF. Precision Precision Comparison experts Recommendable Threshold neighbors Figure 7: Precision of Expert CF (experts line) as compared to the baseline NN CF (neighbors line) as a function of the recommendable threshold σ.
6 5. USER STUDY Although error and recommendation list precision are prominent metrics used for CF evaluation, their relation to the actual quality of the recommendation is unclear. Therefore, we designed a user study to further validate our findings. We designed a web interface that asked users to rate 100 preselected movies. The selection was done by using a stratified random sample on the movie popularity curve: we divided the 500, 000 Netflix movies into 10 equal-density bins and random sampled 10 movies from each bin. We provided a have not seen button so users could voluntarily decide which movies to rate. 57 participants were recruited via advertisement in a large telecommunications company. The participants age ranged from 22 to 47 years, with an average age of 31.2 years. Almost 90% of our participants were in the 22 to 37 age group and most of them were male (79.12%). Note, however, that this demographic group corresponds to the most active group in online applications such as recommender systems [16]. Using the collected ratings and the 8, 000 movies that we considered in the above experiments, we generated 4 top-10 recommendation lists: (i) Random List: A random sequence of movie titles; (ii) Critics choice: The movies with the highest mean rating given by the experts. If two movies had the same mean, we ranked them based on how many experts had rated the movie; (iii) Neighbor-CF: Each survey respondents profile was compared to the ratings of the users in the Netflix data subset: the top-10 list was derived by ordering the unrated movies with predictions higher than the recommendable threshold; and (iv) Expert-CF: Similar to (iii), but using the expert dataset instead of the Netflix ratings. Both neighbor-cf and expert-cf used the same settings: a similarity threshold of 0.01 and a confidence measure of 10. Note that, as we saw in Figure 6, the setting of a given similarity threshold is not unfair to any of the two approaches. Therefore, we choose a value that gives us enough coverage so as to produce enough recommendations. For the settings we chose, the RMSE values are the ones we included in Table 1. Increasing the similarity threshold would reduce coverage, while the ratio between both RMSE s would remain the same. Increasing the confidence threshold would also reduce coverage. Note that the neighbor-cf algorithm would be especially sensitive to changes in the confidence threshold. The coverage of neighbor-cf is 92.9% with the current setting of the threshold, compared to 97.7% in the case of expert-cf. Also note that, as already explained in the previous section, these settings for the neighbor-cf yield an error comparable to standard knn with k = 50. We then asked our participants to evaluate: (i) The overall quality of the recommendation lists; (ii) whether they included items that the user liked; (iii) whether they included items that the user did not like; and (iii) whether they included surprising items. The average number of ratings was of 14.5 per participant. We had to generate the recommendation lists based on such limited user feedback. Therefore, our test conditions are similar to a real setting where the user has recently started using the recommendation service. Our results are thus assessing how well the methods perform in cold-start conditions. Figure 8 includes the responses to the overall list quality. (a) (b) Figure 9: User study responses to whether the list contains movies users would like or would not like. Participants were asked to respond in a 1 to 5 Likert scale as the one included in Figure 8a. Note that we also report the averaged results in Figure 8b. We can see that the expert-cf approach is the only method that obtains an average rating higher than 3. Interestingly, the standard neighbor-cf approach performs almost as badly as a randomly generated list. The only approaches that are qualified as very good are expert based: critics choice and expert-cf. In addition, 50% of the users considered the expert-cf recommendations to be good or very good. Finally, it is important to stress that although an average rate of 3.4 might seem low, this is in a cold-start situation with very limited information from each user. In Figure 9 we report on the replies to the question of whether participants felt the generated lists contained items they knew they would like/dislike. In these cases, we used a 1 to 4 Likert scale, removing the neutral response. We report average results for clarity. In Figure 9a, we again see that the expert-cf approach outperforms the rest of the methods. More than 80% of the users agreed (or fully agreed) that this method generates lists that include movies they like. It is interesting to note that this figure is similar to the assessed overall list quality in Figure 8b. Figure 9b summarizes the ratings to the question the list contains movies I think I would not like. This question is very important: recommending wrong items mines the user s assessment of the system and compromises its usability [17]. Therefore, an important aspect on the evaluation of a recommender system is how often the user is disappointed by the results. The expert-cf approach generates the least negative response when compared to the other methods. Finally, we performed an analysis of variance (anova) to test whether the differences between the four recommendation lists are statistically significant or not. The null hypothesis is that the average user evaluation for the four different lists is the same. The confidence level is set to 99%, such that p-values smaller than 0.01 imply a rejection of the null hypothesis. The p-value for the fours lists is 5.1e 05 and consequently the null hypothesis is rejected. If we leave out the expert-cf algorithm from the analysis, we measure a p- value of In this case, we conclude that the differences on the user satisfaction from the three baseline methods is not statistically significant. The cross-comparison between the expert-cf and the other three algorithms gives p-values of 2.14e 05, 2.4e 03 and 8e 03 for Random, neighbor- CF and Critics Choice respectively. From these results, we
7 (a) Comparison of the responses to overall quality of the recommendation lists generated by 4 methods. (b) Overall quality of the expert-cf method as compared to others, average response Figure 8: Overall quality of different recommendation strategies as assessed by our user study participants. conclude that the differences on the participants satisfaction produced by the expert-cf algorithm cannot be attributed to the sampling of the user study. 6. DISCUSSION In this paper, we have introduced a recommender system that uses a small set of expert ratings to generate predictions for a large population of users. Our experiments are based on using online reviews from movie critics. However, we could also think of a small set of professional raters maintaining a rating database. This is reminiscent but less demanding than content-based recommender systems that rely on experts to manually categorize and label content [18]. The use of experts to generate predictions in a recommender system has been explored by Cho et al. [19]. Their approach, however, is focused on identifying expert users from within a closed community of users, by deriving a domain authority reputation-like score for each user in the data set. The idea of expertise is also related to that of trust. In trust-aware recommender systems the influence of neighbors is weighed by a measure of how trustworthy they are for the current user. The trust measure can be defined and obtained in different ways. For instance, O Donovan and Smyth [20] compute a measure of trust by looking at how well a neighbor has predicted past ratings. As shown in Section 4, our approach does not outperform a naive neighbor-cf approach 6. However, our focus when using external data sources is not as much on prediction accuracy as it is on addressing some of the common problems found in traditional CF recommender systems. In the next paragraphs, we shall describe a few of these problems and discuss how they might be addressed by using a limited set of external experts to generate the predictions (i.e. wisdom 6 It is important to note that there are several improvements to neighbor-cf that would yield better MAE. Similarly, our approach is open to algorithmic improvements that are left for future work. of the few). Data Sparsity: In a standard collaborative recommender system, the user-rating data is very sparse. Although dimensionality reduction techniques offer some help, this problem is still a source of inconsistency and noise in the predictions. Using the wisdom of the few addresses this issue since domain experts are more likely to have rated a large percentage of the items, as shown in Section 2. Noise and Malicious Ratings. Users introduce noise when giving their feedback to a recommender system, both in the form of careless ratings [21] and malicious entries [22, 23], which will affect the quality of predictions. Experts are expected to be more consistent and conscious with their ratings, thus reducing noise. In addition, an expert data set can be immune to malicious, profile-injection attacks as it is an easy to control and stable data set. Cold Start Problem: In a CF system, new items lack rating data and can not be recommended; the same is true when a new user enters the system [24]. Motivated expert users typically rate a new item entering the collection as soon as they know of its existence and therefore minimize item cold-start. In addition, experts should create a less sparse and noisy dataset which should improve the user cold-start problem, as shown in our user study. Scalability: Computing the similarity matrix for N users in an M-item collection is an O(N 2 M) problem. This matrix needs to be updated on a regular basis, as new items and/or users enter the system. Therefore, CF based approaches typically suffer from scalability limitations. While there are several ways to address this issue such as k-means clustering [25], scalability is still an open research problem in CF systems. The wisdom of the few approach is less sensitive to scale, as it creates recommendations from a very reduced set of experts (e.g. 169 experts vs. 500, 000 potential neighbors in the Netflix database). Privacy. Privacy in CF recommender systems is a growing concern and still an area of research [26]. In order to maintain and update the similarity matrix, the system has
8 to transmit all user ratings to a central node where the matrix is computed. This step is not needed in our approach, since the similarity matrix only includes expert data and the target user. In expert-cf, the current experts ratings can be easily transmitted thanks to the reduced size of the matrix, such that all computation is performed locally on the client. This advantage is particularly relevant in a mobile scenario. 7. CONCLUSIONS In this paper, we have proposed an approach to recommend content, based on the opinion of an external source: a reduced number of experts. The proposed method (i.e. the wisdom of the few) is able to predict the ratings of a large population by considering a reduced set of expert ratings. The method s performance is comparable to traditional CF algorithms, even when using an extremely small expert set. We have implemented this approach to predict ratings in the Netflix data set by looking at the ratings of 169 cinema critics, obtaining comparable mean errors. We have also validated the approach in a top-n recommendation setting. Finally, we have shown through a user survey that our method is preferred to standard neighbor-cf in a scenario similar to a cold-start situation. We believe that the proposed approach addresses some of the shortcomings of traditional CF: data sparsity, scalability, noise in user feedback, privacy and the cold-start problem. Although the approach presented in this paper has shown promising results, it has also opened up opportunities for future improvement. First, we have shown the usefulness of expert-cf to a particular domain, but the approach could be implemented in other domains where expert ratings are available. The use of external experts could also be combined with regular CF in order to improve overall accuracy. 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Thank you for choosing an OHFA loan on your journey to homeownership! Our homebuyer guide is a great resource to reference while also viewing the information below to know what to do and what you can expect as you navigate the loan process:
OHFA works with a network of Ohio lenders including banks, credit unions and mortgage companies. Find an OHFA-approved lender in your area, along with tips to help you with the application process, or call us toll-free at 888.362.6432.
Your real estate agent may request a prequalification letter. Please ask your lender; however, a prequalification letter should not be considered a loan approval.
Once you have found a home and obtained contract acceptance, you will complete the loan application. Your lender may require more documentation during the application process. Ask your lender for an estimate of all costs associated with your loan.
OHFA requires free homebuyer education for all borrowers once your OHFA loan is reserved. Information on OHFA's streamlined homebuyer education program is available on our website. You may also complete a course offered by any HUD-approved counseling agency in Ohio.
The lender verifies your credit report, income, assets and payment history and determines if your application qualifies for an OHFA loan. A home appraisal will be completed during this time by the lender to determine if the value of the property meets the mortgage loan amount.
The closing will be scheduled once your loan is approved. Closing usually occurs 30 to 45 days after completing the loan application. Once you sign all of the final documents with the title/escrow company, you'll receive the keys to your new home!
Note: Other steps in the homebuying process may occur in partnership with your real estate agent.
The Ohio Housing Finance Agency | 57 East Main Street | Columbus, Ohio 43215
Toll Free 888.362.6432 | Phone 614.466.7970 | Fax 614.644.5393 | TDD 800.750.0750
Mike DeWine, Governor, State of OhioLegal Statement and Policies

subset: commoncrawl


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The image above as well as the first one below were started the same, using watercolor crayons (coloring multiple shades directly on the rubber), a spritz of water onto the rubber, stamp on watercolor paper, and my spray tool spritzing some caramel and green ink. Oh, and also my Frosted Lace Stickles for sparkle. I decided to make the first card a torn window and age it with some sponging. The second I went much simpler, but thought this was still nice, just a little different personality.
This next one you may have to click on to get a better look. I ironed a piece of velveteen paper onto the rubber. It embosses the velvet. I chose to keep this simple to let the velvet stand out. It's kinda rustic and elegant together.
Lastly, I went for a sparkly, more monochromatic look with caramel, vanilla, and a bit of khaki green? I tied in the caramel color with copper brads, and I used Star Dust Stickles for some sparkle. The background stamp is the old holiday print background from Stampin' Up. My lighting has gotten bad in my craft room, so my pictures are seemingly getting worse and worse. I wish you could see these in person!
I'm going to be selling some things, if you're local and want to see, let me know. I have creations as well as stamps. If you're not local, let me know and I can send you a list and any pics by request.
More ideas hopefully to share this weekend!


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The Last Post
You may have noticed that this is a place I don't stop by as much these days.
When havenspace was born, I was in a very different place,
geographically and mentally.
It was time for a change.
|The Birthday Girl
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1 John 4:16
And so we know and rely on the love God has for us.
God is love. Whoever lives in love lives in God, and God in him.
Matthew 22:37-39
Jesus replied: "Love the Lord your God with all your heart and with all your soul and with all your mind.'This is the first and greatest commandment. And the second is like it: 'Love your neighbor as yourself."
Just love each other. 🙂
"I Stand Amazed"–Bart Millard


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The Watch List highlights the must-know storylines for the upcoming week in tennis. In the first week of the 2015 season, the action kicks off with Maria Sharapova, Ana Ivanovic, Roger Federer and Kei Nishikori at the Brisbane International, while Serena Williams, John Isner, Eugenie Bouchard and others opted to start the year at the Hopman Cup.
Brisbane International
Brisbane once again serves host to the biggest tournament of the week, with a jam-packed field of men and women. The women lead the way with a Premier-level event headlined by No. 2 Maria Sharapova and No. 5 Ana Ivanovic, while Roger Federer and Kei Nishikori lead the ATP 500 field for the men. First round matches begin on Sunday (Saturday evening U.S. time). Here's how the draws break down:
Two-time defending champion Serena Williams has opted to skip the tournament this year in favor of Hopman Cup, meaning the field is as wide open as ever since the tournament was upgraded to a Premier-level in 2012. Sharapova has never won a lead-up event ahead of the Australian Open and her best result in Brisbane was last year, where she lost to Serena in the semifinals. She has a bye in the second round and will face either big-serving Sabine Lisicki or a qualifier. Angelique Kerber and Jelena Jankovic are also in her half.
The bottom half of the draw, led by Ivanovic, is far more unpredictable. Victoria Azarenka, Dominika Cibulkova, Madison Keys and Sam Stosur are all lurking. Ivanovic opens against either Zhang Shuai or Jarmila Gajdosova, before possibly playing her good friend Andrea Petkovic. But the two to keep an eye on here are Azarenka and Keys, two players we highlighted in this post. Both have tricky early round matches. Keys carries a 2-0 record into her match against last year's Australian Open finalist Cibulkova, while Azarenka goes into her opener against Pliskova as the underdog on paper, with the young Czech standout at No. 21. Currently ranked No. 31, an early loss for the two-time Australian Open champion could mean she'll go into her favorite Slam unseeded. The same is at stake for Keys, who could lose her top 32 seed with an early loss.
Potential matches to watch: Azarenka vs. Karolina Pliskova (first round), Keys vs. Cibulkova (first round), Stosur vs. Varvara Lepchenko (first round), Kerber vs. Caroline Garcia (first round), Garbine Muguruza vs. Bethanie Mattek-Sands (first round), Sharapova vs. Lisicki (second round), Muguruza vs. Azarenka/Pliskova (second round), Stosur vs. Keys/Cibulkova (second round).
A finalist last year, Federer hopes to continue his fantastic run of form over the last 12 months and build towards a chance to take over the No. 1 ranking at some point this season. Federer thrived on the lightning quick courts last year and won't mind his draw again this year. He gets a first round bye and will open against either a qualifier or Aussie wildcard John Millman. If the draw holds he would face Gilles Simon in the third round and Grigor Dimitrov in the semifinals.
Nishikori's bottom half of the draw is far trickier. Milos Raonic, Julien Benneteau, Kevin Anderson, defending champion Lleyton Hewitt, Sam Querrey, Bernard Tomic and Steve Johnson all ended up in his half. Nishikori will open his tournament against either Johnson or Marinko Matosevic, and could face some big servers in Raonic or Anderson en route to a final.
Potential matches to watch: Tomic vs. Sam Querrey (first round), Julien Benneteau vs. Thanasi Kokkinakis (first round), Dimitrov vs. Jeremy Chardy (second round), Anderson vs. Hewitt (second round), Benneteau vs. Tomic/Querrey (second round), Dimitrov vs. Alexandr Dolgopolov (third round), Federer vs. Simon (third round).
Hopman Cup
Who doesn't love Hopman Cup? The ITF team-based competition pits co-ed teams representing their home nation against each other, with each tie consisting of a women's singles match, a men's singles match and a mixed doubles match. This year's competition, which begins on Sunday in Australia, delivers a field of intriguing teams. The U.S. will have its two, big-serving No. 1s paired up in Serena and John Isner. The field also includes Andy Murray, Eugenie Bouchard and Agnieszka Radwanska. Two Groups of four teams will compete in a round-robin format with the winner of each group advancing to the final.
Group A
Canada: Bouchard & Vasek Pospisil
Czech Republic: Lucie Safarova & Adam Pavlasek
Italy: Flavia Pennetta & Fabio Fognini
United States: Serena & Isner
Group B
Australia: Casey Dellacqua & Matt Ebden
France: Alize Cornet & Benoit Paire
Great Britain: Heather Watson & Andy Murray
Poland: Radwanska & Jerzy Janowicz.
The U.S. will play Canada on Tuesday, in what may be the most anticipated tie of the week. Serena routed Bouchard at the WTA Finals in October, while Isner and Pospisil have had some memorable showdowns. Pospisil leads thier head-to-head 2-1 and all the matches have gone three sets. Then comes the mixed doubles which should undoubtedly be entertaining. Also worth watching are Great Britain vs. Poland, Poland vs. France and the United States vs. Italy.
ASB Classic
Caroline Wozniacki and Sara Errani are the top two seeds in Auckland, New Zealand, with a field that includes last year's finalist Venus Williams. This is a very beatable field for Wozniacki, who is looking to get back to her title-winning ways. Despite her fantastic second half of the season, the Dane won just one small title. Wozniacki is already on the short-list of Australian Open favorites and she needs the trophy to prove she belongs there.
There are a number of Americans to keep an eye on as well. Venus' run to the final here last year foreshadowed a solid year that saw her back in the top 20. She opens against Jana Cepelova, the young Slovakian who upset her sister last year in Charleston. Sloane Stephens and CoCo Vandeweghe are also starting their seasons in Auckland, as is Taylor Townsend. Townsend opens up against Yanina Wickmayer and a few wins could see her finally break into the Top 100.
Qatar Open
Novak Djokovic and defending champion Rafael Nadal are the top two seeds in Doha, with both men playing their only scheduled tune-up tournament before the Australian Open. Djokovic was forced to withdraw from an exhibition in Abu Dhabi over the weekend due to a fever, while Nadal won just two games against Murray at the same exhibition before beating Stan Wawrinka in straight sets. Tomas Berdych and David Ferrer round out the top four seeds.
Chennai Open
Stan Wawrinka begins his season with a return to Chennai to defend his title. It's the only tournament he's won more than once, having also won the title in 2011. Wawrinka, Feliciano Lopez, Roberto Bautista Agut, and David Goffin are the top four seeds.


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Japanese Miniso stores with a price tag of $2.99 for most of the items is a new threat to dollar stores.
The lifestyle brand that offers, small sized quality products for low price has launched its first store in Pasaden, California on April 22, 2017. Miniso meaning small stuff was created by Japanese designer Miyake Janya and Chinese entrepreneur Ye Guofu.
The store offers wide range of products such as toys, bags, cosmetics, gadgets, stationery, kitchenware, etc, in very appealing color and trendy design. Customers find the products value for money.
Miniso opened three stores in Canada this year and planning launch up to 50 stores in Alberta, Ontario and Quebec by the end of 2018. It has ambitious plans to start 500 stores in Canada in the next few years.
The discount chain founded in 2013 has established its space in growing markets such as Vietnam, Indonesia, Taiwan, Philippines etc. The total store counts for Miniso is around 2000 stores in 60 countries. It has wider plans to launch more than 10,000 stores in 100 or more countries by in next two years. It targets global sales turnover of more than $15 billion.
For comments and feedback contact: email@example.com
Business News
New Drugs Approved In December
10 Gene Therapy Companies Unignorable in 2019
10 RNA Therapeutics Companies To Watch In 2019


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11:15am PT by Erin Carlson
Rachel Zoe's Ex-Assistant Brad Goreski Speaks Out On Estrangement From His Former Boss
The fashionista flame war continues as Rachel Zoe's once-beloved former assistant, Brad Goreski accuses her of "unjust" behavior.
Some background: Goreski used to be the super-stylist and Hollywood Reporter cover girl's wingman on her Bravo reality show The Rachel Zoe Project. (He also worked in her L.A. office in real life, too.) Last year, Goreski left Zoe's growing empire to branch out on his own as a stylist. But, gasp, he took one of her A-list clients, Demi Moore, with him on his way out the door. Then Bravo gave him a fashion-focused reality series, called It's a Brad, Brad World, spinning off Zoe's star vehicle.
In a new teaser for the show, debuting Jan. 2, Goreski discussed his fallout with Zoe and how he's never met her baby son, Skyler, who's 9 months old. "We don't speak to each other," he said in the Bravo clip, via People.com. "I would love to be able to speak with her. I have not seen the baby because I have not been invited."
Last season on The Rachel Zoe Project, Zoe referenced Goreski's departure, claiming he told her and husband-slash-business partner Rodger Berman that he was departing to slow down a bit -- not to launch a solo career. "It's not the first time I've been used. I would say it's going to be the last time, though," she told the cameras in an episode that aired in September.
Meanwhile, Goreski would not elaborate on what led to his former boss' icy treatment. He said has "an idea" but believes "those reasons are unjust."
"I have conducted myself with honesty, with dignity and with grace," Goreski added. "And I will continue to do so no matter what is said about me."
Erin email@example.com @thr


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What do you do in this case? My solution: double fisting.
|Fuck today.
In between losing my phone and losing my keys, I fell victim to the Steam sale. Nothing grand because honestly, I already own quite a few of those games for consoles. I picked a few smaller games: Ben There, Dan That!; Costume Quest; Dear Esther; Stacking; Time Gentlemen, Please!; and The Tiny Bang Story.
The Tiny Bang Story
Before I even started drinking, I think I finished The Tiny Bang Story. Think as in I'm not exactly certain. As a gamer, I need closure. I need those rolling titles and the possibility of an ending cutscene (a gift for those who are patient and can wait without mashing buttons). I didn't get that with The Tiny Bang Story. The puzzles are fun but not so difficult that I needed to peek at a guide. Everything is pretty intuitive with the controls (click click click, motherfucker). I'd give this a 4/5 because it calmed the angry drunk within me.
Dear Esther
After I lost my keys, the angry drunk needed to escape. I'm writing this as I'm playing Dear Esther. I'm double fisting (see picture above), I'm unhappy, and I'm gaming. Having decided I have too much ADD for "Time Gentlmen, Please!", I moved onto Dear Esther. So far, I'm distracted by every single piece of litter I find on the island. But hey, I found the chemical structure for CH3CH2OH. Or at least that's what how I think it should be written (and if I could subscript the font on this article, I would). It's been a while since my organic chemistry days.
Lighthouse? Check. Lots of paint cans strewn everywhere? Check.
This game reminds me of Myst. I tried to play that when I was ten or so. I was amazed by my uncle who seemed to play it without problem. I couldn't even figure out the controls. Now I'm a little more advanced. Dear Esther didn't prompt me with any "A/W/S/D to move", they wanted me to solve shit myself. Went into controls to alter the defaults (Just to see what the fuck each button does!). Apparently I can "swim up" so I tried walking into the water to test that. That was a world of confusion. THE Q. IT DOES NOTHING.
The writing and voice acting remind me of Colin Firth as Mr Darcy in Pride and Prejudice. What he says is just a little bit long winded and never to the point. He's going to be a 13 year old fanfic writer and take the long winded route to tell you something. I almost expect, as I'm wandering along the cliffs, that the character will soon start telling me about the dustbowl conditions in the 30's and how he had to make rope by knotting together scraps of rags. And I shit you not, I've heard that story before.
Great he's talking about the bible now, like I've heard-- OH LOOK, A SHIPWRECK. The environment is a little distracting from the story for me. I tried wading out to the shipwreck and got about 80% there before I submerged. The water is pitch black and Q still doesn't do a thing. "Come back" whispers the narrator. I guess I'll land on shore and take off where I left off.
The only sign of life: a seagull and some ambiance piano music. This guy writes like a jaded teenager. "Dear Esther..."
Oh great, now I accidentally hit F7 instead of F6 after a load screen and now I get to hear that tearing teenage "Dear Esther" all over again. I'll end this here, Dear Readers.
Any recommendations for future Steam sales purchases?
Tiny Bang Story: 2 hours. No alcohol consumed.
Dear Esther: N hours. Lots more to be consumed as I continue to play.


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I want to give it a proper goodbye.
*cue Frozen soundtrack*
Let's not call this a break up, rather...a conscious uncoupling.
haha. I'll be honest, I've been wanting an excuse to use that line for a bit now...so badly. ;)
So sad that you're leaving!! I need to follow you on Pinterest b/c I am not techy enough yet with an i phone. I will really miss your blog, Camille!ReplyDelete
We'll miss you!! It's good until it isn't anymore and you are so smart to realize this and move onto many more other good things that are happening in your life :)ReplyDelete
I'll miss your blogging! Enjoy your time with your kiddos, and best of luck with whatever the future holds. :)ReplyDelete
Ohhhhhh! I totally understand your reasoning - good on you!! But I will miss you and your fashion sense. I've gotten great (and plausible!) ideas here. Heading over to insta right now. E-hugs to you and your beautiful littles.ReplyDelete
So much time and energy goes into a blog. There are so many days I want to toss in the towel!
In a few years I will be able to (my goal!), but for now, thankful for it.
It's hard to cut ties with something you have put heart into but I bet you feel so much better already. Glad we can keep in touch on instagram!
Thanks sharing!ReplyDelete
I love your blog and will miss it. I totally understand though. I've been struggling with the same things - although I still have to work the 9-5 to pay the bills.
Good luck!
Love this :)
White Hair In This Field Performance Car B15 Piston Ring Set
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I really enjoy this.
Richard D.
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I'm personally a big fan of lifeinmodification blog. Thanks for sharing this post.
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Gariyaband District : Chhattisgarh
About District
The district came into existence from January 1, 2012. The district covers an area of 822.861 square kilometers. The total population is around 5,97,653 according to 2011 census. The district is divided into one subdivision, 5 tehsils and 711 villages. The district headquarters, Rjim is located in the south east of Raipur and the distance is around 90 km. The places of tourist interest in the districts are Rajim, Jtmi, Gtarani waterfall, Bhuteswaranath , Udnti-Sitandi Tiger Reserve Gariaband, and Sikasar reservoir Gariaband.


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Safety Foamy Car Tire Cleaner Spray 500ml Dressing Cleaning
Captain Tyre Foamy Renew contains specific ultra-high macromolecule ingredient. It can quickly provide a protective film on the surface of the tyre that have a specific function of decontamination, glazing and protection in one time.. The product contains UV ray absorbefacient, so it can avoid UV damage to prolong the usage life of the tyre. All ingredients of the product are green materials, and they are of no harm to human and environment.
Item No.:
Can Size:
65mm D x 240mm H
650ml x 12pcs/ctn
Carton Size:
275x205x290 mm
20ft Container Loading
1845 cartons
After cleaning, spray this product equally on sidewall of tyre. This product can be used on wet or dry surface of tyre.
1. Keep away from heat, flame, spark and other source of ignition.
2. Store in a cool, dry place (45°C); Avoid direct sunlight.
3. Do not clash, puncture, or incinerate the can.
4. Keep out of reach of children;
CAPTAIN Car Care (car cleaning) Products include:
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11. Tyre Fix (Tyre Sealer & Inflator)
more items available at our website :www.ilikegroup.com
Some key words about our company:
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9.More than 90 big distributors in Chinese market
10.With more than 10 sole agents in the world
Safety Foamy Car Tire Cleaner Spray 500ml Dressing Cleaning Products Images


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Okay, this proves that they just don't get it! How can they measure and study prayer when they don't even understand it??! God is not some genie in a bottle that you rub the right way and get your wish! There is no "formula" or "equation". It is a relationship. A conversation. A meeting of the minds and the wills.
And they think they can set up a test that will measure God's effectiveness? Remember the temptation of Christ? When Satan wanted him to throw himself off the temple top and let the angels run and catch him, Christ said you don't put the Lord God to foolish tests.
Yes, prayer could be proven. But I don't believe God will allow it. If He did then there would be no faith required. The Bible tells us that Salvation comes by faith. And the salvation of mankind is God's most important mission. He would not jeopardize it.
As for their doubt and "explanations" of the miracles God performs I feel like the little boy in the joke: He's on the park bench reading where Moses led the Israelites thru the parted Red Sea. And he starts praising God and His greatness. A man on the bench asks him what he's so excited about. When the boy tells him, the man tells him that science has proven that the water level was probably much lower then, just a few inches and was no real feat for them to walk across. The boy, quiet, returns to Exodus. In a few minutes he goes to jumping and hollering and praising God all the more. The startled man asked, "Why, didn't I just explain to you this was foolishness?" But the boy said, "Mister, God drowned the whole Egyptian army in just a few inches of water!"
Thank God there are scientists out there that are also Christians. That can see and accept the hand of God, and admit wedon't know it all and never will in this world.
As for the others, that's okay. Let 'em do their thing. One day their knee will bow and their tongue confess.


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ENTERPRISE EDITORIAL: Lions International celebrates 100 years serving communities
Lions Clubs International celebrates a century of service this year and has grown to be the largest service organization in the world. Melvin Jones founded the club on June 7, 1917, and now 100 years later, there are over 45,000 clubs and more than 1.3 million members in 203 countries worldwide.
Jones, a Chicago businessman, asked, with regards to his colleagues, "What if these men who are successful because of their drive, intelligence and ambition, were to put their talents to work improving their communities?"
Lions clubs across the world operate under the motto: "We Serve."
In 1925, Helen Keller beseeched Lions to become "Knights of the Blind" and her eloquent plea provided Lions with their primary mission. The Lions started the Leader Dogs for the Blind in 1939. Women were invited to become members in 1987.
From focusing on vision, Lions Clubs International now addresses, diabetes, hearing and service dogs. In 1968, Lions Clubs International Foundation was established and this addresses the needs of people around the globe who are impacted by disasters such as floods, earthquakes, tornados, wildfires, etc.
The Park Rapids Lions Club received its charter on June 23, 1954, at a meeting held at the Park Rapids Municipal building. Sponsored by the Wadena Lions, the club was chartered with 20 members and elected Robert Brown as their first president.
For 63 years, the Park Rapids Lions continue to serve the community and join this year in celebrating the international club's 100th birthday. The Park Rapids club currently has 47 members and since 1954 has provided volunteer services and financial assistance to those in need throughout the Park Rapids area.
The Leader Dogs for the Blind is one of the major programs to receive club support. Since 2007, the club has contributed about $10,000 to this program.
While the focus internationally is on sight related projects, locally the club contributes to projects such as spring leaf and lawn litter pickup and disposal, scholarships for local high school seniors, Headwaters 100 Bike Ride, financial and volunteer support for Kinship, Food Angels Backpack Program, fountain and tree planting at Depot Park, Hubbard County Food Shelf, piano project, eyeglass purchases for people unable to pay, specialized tricycle for local youth, hearing aids for local youth, ditch pick up, Salvation Army bell ringers, pre-Memorial Day cleanup at Greenwood Cemetery, and many more.
The longest lived community project sponsored by the Lions is the annual spring leaf and lawn litter pick-up on Community Service Day. This project began in the spring of 1974.
To help support the many projects one of the club's major fundraisers is the Park Rapids Lions Garage Sale, this year on Saturday, June 3, from 9 a.m. to 4 p.m.
Local vision related projects include vision assist reading machines for persons with eyesight issues and used eyeglass collection boxes at local businesses. Local Lions members also build wheelchair accessible ramps for local residents.
The Park Rapids Lions over the past 30 years have made financial contributions to many community facilities, recreation and betterment projects and community activities. They contribute with dollars spread out around the area, as well as provide manpower to numerous community events each year. They support programs to benefit youth in the area and help support school programs.
Fifteen local Lions in Park Rapids have received the distinguished Melvin Jones Award for outstanding dedication and service in the Lions organization.


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For today's trip, we carpooled to Utah, towards Cowboy Pass. The air was not clear; like Colorado, there've been fires burning in Nevada. The wind was up and humidity was down, so smoke blew across mountains and valleys from north Nevada.
|Robinson Ranch site
|Searching the wash at the Robinson Ranch site
We never became certain of the source rock, though we looked for it. While searching, I did find some interesting concretions to photograph. And Dennis found a curious lizard:
|Rock art
|Click for larger to see Lizzie
|Holdfast? and branched solitary coral
The second stop of the day was also in west Utah, Bishop Springs. This particular locality was a lone mound in a wide open valley.
|Bishop Springs site
|An eye-catching concretion
In several places, there were fossils in slabs too big and hard to bring home - beautiful horn
corals, interesting brachiopods, and a lone gastropod. The brachiopods were on concretions we speculated might have been part of a burrow system.
|Coral Slab
|Brachiopods (a few I circled) and possible burrows
The slab of horn corals pictured below shows an interesting story of preservation. Apparently the center of some of these will be replaced differently than the outer portion. The center is replaced with chert which is harder than the source rock. As the source rock is worn away, the chert is left exposed.
|Interesting preservation of coral
The final stop of the day was the Ward Charcoal Kilns. The kilns were used to burn wood into charcoal which was used in local smelters.
|Ward Charcoal Kilns
Since I didn't get to this blog last night and am writing this in the morning, the weather has cooled. I am glad I am in a motel: