Recommendation system is a common demand in daily life and matrix completion is a widely adopted technique for this task. However, most matrix completion methods lack semantic interpretation and usually result in weak-semantic recommendations. To this end, this paper proposes a @math emantic @math nalysis approach for @math ecommendation systems @math , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item. @math learns semantic representations of users items merely from user ratings on items, which offers a new path to recommendation by semantic matching with the learned representations. Extensive experiments demonstrate @math outperforms other state-of-the-art baselines substantially.
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@cite_9: Social tagging becomes increasingly important to organize and search large-scale community-contributed photos on social websites. To facilitate generating high-quality social tags, tag recommendation by automatically assigning relevant tags to photos draws particular research interest. In this paper, we focus on the personalized tag recommendation task and try to identify user-preferred, geo-location-specific as well as semantically relevant tags for a photo by leveraging rich contexts of the freely available community-contributed photos. For users and geo-locations, we assume they have different preferred tags assigned to a photo, and propose a subspace learning method to individually uncover the both types of preferences. The goal of our work is to learn a unified subspace shared by the visual and textual domains to make visual features and textual information of photos comparable. Considering the visual feature is a lower level representation on semantics than the textual information, we adopt a progressive learning strategy by additionally introducing an intermediate subspace for the visual domain, and expect it to have consistent local structure with the textual space. Accordingly, the unified subspace is mapped from the intermediate subspace and the textual space respectively. We formulate the above learning problems into a united form, and present an iterative optimization with its convergence proof. Given an untagged photo with its geo-location to a user, the user-preferred and the geo-location-specific tags are found by the nearest neighbor search in the corresponding unified spaces. Then we combine the obtained tags and the visual appearance of the photo to discover the semantically and visually related photos, among which the most frequent tags are used as the recommended tags. Experiments on a large-scale data set collected from Flickr verify the effectivity of the proposed solution.
@cite_1: Abstract Given the increasing applications of service computing and cloud computing, a large number of Web services are deployed on the Internet, triggering the research of Web service recommendation. Despite of service QoS, the use of user feedback is becoming the current trend in service recommendation. Likewise in traditional recommender systems, sparsity, cold-start and trustworthiness are major issues challenging service recommendation in adopting similarity-based approaches. Meanwhile, with the prevalence of social networks, nowadays people become active in interacting with various computers and users, resulting in a huge volume of data available, such as service information, user-service ratings, interaction logs, and user relationships. Therefore, how to incorporate the trust relationship in social networks with user feedback for service recommendation motivates this work. In this paper, we propose a social network-based service recommendation method with trust enhancement known as RelevantTrustWalker. First, a matrix factorization method is utilized to assess the degree of trust between users in social network. Next, an extended random walk algorithm is proposed to obtain recommendation results. To evaluate the accuracy of the algorithm, experiments on a real-world dataset are conducted and experimental results indicate that the quality of the recommendation and the speed of the method are improved compared with existing algorithms.
@cite_5: Social recommendation problems have drawn a lot of attention recently due to the prevalence of social networking sites. The experiments in previous literature suggest that social information is very effective in improving traditional recommendation algorithms. However, explicit social information is not always available in most of the recommender systems, which limits the impact of social recommendation techniques. In this paper, we study the following two research problems: (1) In some systems without explicit social information, can we still improve recommender systems using implicit social information? (2) In the systems with explicit social information, can the performance of using implicit social information outperform that of using explicit social information? In order to answer these two questions, we conduct comprehensive experimental analysis on three recommendation datasets. The result indicates that: (1) Implicit user and item social information, including similar and dissimilar relationships, can be employed to improve traditional recommendation methods. (2) When comparing implicit social information with explicit social information, the performance of using implicit information is slightly worse. This study provides additional insights to social recommendation techniques, and also greatly widens the utility and spreads the impact of previous and upcoming social recommendation approaches.
@cite_12: Social recommender systems exploit users' social relationships to improve the recommendation accuracy. Intuitively, a user tends to trust different subsets of her social friends, regarding with different scenarios. Therefore, the main challenge of social recommendation is to exploit the optimal social dependency between users for a specific recommendation task. In this paper, we propose a novel recommendation method, named probabilistic relational matrix factorization (PRMF), which aims to learn the optimal social dependency between users to improve the recommendation accuracy, with or without users' social relationships. Specifically, in PRMF, the latent features of users are assumed to follow a matrix variate normal (MVN) distribution. The positive and negative dependency between users are modeled by the row precision matrix of the MVN distribution. Moreover, we have also proposed an efficient alternating algorithm to solve the optimization problem of PRMF. The experimental results on real datasets demonstrate that the proposed PRMF method outperforms state-of-the-art social recommendation approaches, in terms of root mean square error (RMSE) and mean absolute error (MAE).
leverages social information to enhance recommendation such as relationship between users, personalized profiles or movies' attributes. There list some latest researches: SR @cite_5 , PRMF @cite_12 , geo-specific personalization @cite_9 , social network based methods @cite_1 and other social context integration methods @cite_4 .