An embedding approach for context-aware collaborative recommendation and visualization

Recommender system has been widely used in e-commerce systems nowadays. Current methodologies focus on predicting users' preferences from their previous ratings. Although the prediction is largely helpful, it gives limited insight to managers of e-commerce systems on how to utilize the interactions between users and items for designing new business and marketing strategies. Besides, big data collected by e-commerce systems raise another challenge in recommendation - how to incorporate large amount of additional information such as the contexts where the rating or buying event takes place. In this paper, we propose a novel method to simultaneously tackle the two challenges above based on the concept of embedding, by deriving a general distance-dependent rating model that characterizes the relationship between user and item with respective to the embedding space. The generalized model allows us to incorporate contextual information into the recommender system for rating prediction and item recommendation. We show that our embedding model is comparable to state-of-the-art context-aware recommendation algorithms in terms of accuracy, while allowing visualization as an analytics tool which gives intuitive insights to the recommendation in an understandable way. In addition, our algorithm also allows efficient recommendation by leveraging the neighborhood structure of the embedding space. We demonstrate the advantages of our method with experiments and results show that context-aware embedding is a promising approach for context-aware recommender systems.