Recommender systems (RS) help users to find their appropriate items among large volumes of information. Among the different types of RS, context-aware recommender systems aim at personalizing as much as possible the recommendations based on the context situation in which the user is. In this paper we present an approach integrating contextual information into the recommendation process by modeling either item-based or user-based influence of the context on ratings, using the Pearson Correlation Coefficient. The proposed solution aims at taking advantage of content and contextual information in the recommendation process. We evaluate and show effectiveness of our approach on three different contextual datasets and analyze the performances of the variants of our approach based on the characteristics of these datasets, especially the sparsity level of the input data and amount of available information.
@cite_18: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
@cite_22: Context aware recommender systems (CARS) adapt the recommendations to the specific situation in which the items will be consumed. In this paper we present a novel context-aware recommendation algorithm that extends Matrix Factorization. We model the interaction of the contextual factors with item ratings introducing additional model parameters. The performed experiments show that the proposed solution provides comparable results to the best, state of the art, and more complex approaches. The proposed solution has the advantage of smaller computational cost and provides the possibility to represent at different granularities the interaction between context and items. We have exploited the proposed model in two recommendation applications: places of interest and music.
@cite_11: Context-aware recommender systems CARS take context into consideration when modeling user preferences. There are two general ways to integrate context with recommendation: contextual filtering and contextual modeling. Currently, the most effective context-aware recommendation algorithms are based on a contextual modeling approach that estimate deviations in ratings across different contexts. In this paper, we propose context similarity as an alternative contextual modeling approach and examine different ways to represent context similarity and incorporate it into recommendation. More specifically, we show how context similarity can be integrated into the sparse linear method and matrix factorization algorithms. Our experimental results demonstrate that learning context similarity is a more effective approach to context-aware recommendation than modeling contextual rating deviations.
CAMF @cite_22 is an extension of matrix factorization @cite_18 , that tries to take into account the context situation of users by integrating additional model parameters in the matrix factorization equation. @cite_11 proposed a similarity-based version of this approach.