A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Context-aware movie recommendations: An empirical comparison of pre-filtering, post-filtering and contextual modeling approaches

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39878-0_13Proceedings of 14th International Conference, EC-Web 2013, Prague, Czech Republic, August 27-28, 2013.Context-aware recommender systems have been proven to improve the performance of recommendations in a wide array of domains and applications. Despite individual improvements, little work has been done on comparing different approaches, in order to determine which of them outperform the others, and under what circumstances. In this paper we address this issue by conducting an empirical comparison of several pre-filtering, post-filtering and contextual modeling approaches on the movie recommendation domain. To acquire confident contextual information, we performed a user study where participants were asked to rate movies, stating the time and social companion with which they preferred to watch the rated movies. The results of our evaluation show that there is neither a clear superior contextualization approach nor an always best contextual signal, and that achieved improvements depend on the recommendation algorithm used together with each contextualization approach. Nonetheless, we conclude with a number of cues and advices about which particular combinations of contextualization approaches and recommendation algorithms could be better suited for the movie recommendation domain.This work was supported by the Spanish Government (TIN2011-28538-C02) and the Regional Government of Madrid (S2009TIC-1542

Matrix Factorization Techniques for Context-Aware Collaborative Filtering Recommender Systems: A Survey

open access articleCollaborative Filtering Recommender Systems predict user preferences for online information, products or services by learning from past user-item relationships. A predominant approach to Collaborative Filtering is Neighborhood-based, where a user-item preference rating is computed from ratings of similar items and/or users. This approach encounters data sparsity and scalability limitations as the volume of accessible information and the active users continue to grow leading to performance degradation, poor quality recommendations and inaccurate predictions. Despite these drawbacks, the problem of information overload has led to great interests in personalization techniques. The incorporation of context information and Matrix and Tensor Factorization techniques have proved to be a promising solution to some of these challenges. We conducted a focused review of literature in the areas of Context-aware Recommender Systems utilizing Matrix Factorization approaches. This survey paper presents a detailed literature review of Context-aware Recommender Systems and approaches to improving performance for large scale datasets and the impact of incorporating contextual information on the quality and accuracy of the recommendation. The results of this survey can be used as a basic reference for improving and optimizing existing Context-aware Collaborative Filtering based Recommender Systems. The main contribution of this paper is a survey of Matrix Factorization techniques for Context-aware Collaborative Filtering Recommender Systems