Context-aware Music Recommender System Based on Implicit Feedback

Context-Aware Music Recommender Systems

Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user [20]. In the music domain recommender systems can support information search and discovery tasks by helping the user to find relevant music items, for instance, new music tracks, or artists that the user may not even know [18, 9]. Several techniques have been proposed but most of t...

Electronic Books Recommender System Based On Implicit Feedback Mechanism

In recent years recommender systems (RSs) has gained popularity to solve the problem of web information overload and redundancy. Recommendation system helps users in finding the contents of their interest with minimum efforts. Even though most of the systems use explicit rating to recommend the content of users interest. When reading the electronic books performance of user gets affected becaus...

On Privacy-preserving Context-aware Recommender System

Privacy is an important issue in Context-aware recommender systems (CARSs). In this paper, we propose a privacy-preserving CARS in which a user can limit the contextual information submitted to the server without sacrificing a significant recommendation accuracy. Specifically, for users, we introduce a client-side algorithm that the user can employ to generalize its context to some extent, in o...

Context-Aware Recommender System Based on Boolean Matrix Factorisation

In this work we propose and study an approach for collaborative filtering, which is based on Boolean matrix factorisation and exploits additional (context) information about users and items. To avoid similarity loss in case of Boolean representation we use an adjusted type of projection of a target user to the obtained factor space. We have compared the proposed method with SVD-based approach o...

Context-aware factorization methods for implicit feedback based recommendation problems