Using Contextual Information from Topic Hierarchies to Improve Context-Aware Recommender Systems

Unlike the traditional recommender systems, that make recommendations only by using the relation between user and item, a context-aware recommender system makes recommendations by incorporating available contextual information into the recommendation process as explicit additional categories of data to improve the recommendation process. In this paper, we propose to use contextual information from topic hierarchies to improve the accuracy of context-aware recommender systems. Additionally, we also propose two context-aware recommender algorithms for item recommendation. These are extensions from algorithms proposed in literature for rating prediction. The empirical results demonstrate that by using topic hierarchies our technique can provide better recommendations.