Context suggestion: empirical evaluations vs user studies

Abstract

Recommender System has been successfully applied to assist user's decision making by providing a list of recommended items. Context-aware recommender system additionally incorporates contexts (such as time and location) into the system to improve the recommendation performance. The development of context-aware recommender systems brings a new opportunity - context suggestion which refers to the task of recommending appropriate contexts to the users to improve user experience. In this paper, we explore the question whether user's contextual ratings can be reused to produce context suggestions. We propose two evaluation mechanisms for context suggestion, and empirically compare direct context predictions and indirect context suggestions based on a movie data that was collected from user studies. The experimental results reveal that indirect context suggestion works better than the direct context prediction, and tensor factorization is the best approach to produce context suggestions in our movie data.