Towards context-sensitive collaborative media recommender system

With the rapid increase of social media resources and services, Internet users are overwhelmed by the vast quantity of social media available. Most recommender systems personalize multimedia content to the users by analyzing two main dimensions of input: content (item), and user (consumer). In this study, we address the issue of how to improve the recommendation and the quality of the user experience by analyzing the contextual aspect of the users, at the time when they wish to consume multimedia content. Mainly, we highlight the potential of including a user's biological signal and leveraging it within an adapted collaborative filtering algorithm. First, the proposed model utilizes existing online social networks by incorporating social tags and rating information in ways that personalize the search for content in a particular detected context. Second, we propose a recommendation algorithm to improve the user experience and satisfaction with the use of a biosignal in the recommendation process. Our experimental results show the feasibility of personalizing the recommendation according to the user's context, and demonstrate some improvement on cold start situations where relatively little information is known about a user or an item.