An Enhanced Memory-Based Collaborative Filtering Approach for Context-Aware Recommendation

Recommender systems have been advocated in different domains for years and there are various recommender systems developed in different application services. In addition to using recommendation techniques, it is helpful to employ contextual information in determining the relevance of an item to a user's needs. To enhance recommendation performance, we present in this study a new approach that, in a direct way, integrates different types of contextual information and user ratings with computational methods. To verify the proposed approach in collaborative recommendations, we conduct a series of experiments to evaluate its performance. The results show that the proposed context aware method outperforms other conventional approaches. Moreover, we implement a mobile recommendation system on a cloud platform to show that our approach can be used to develop a real-world application.