UIContextListRank: A Listwise Recommendation Model with Social Contextual Information

Abstract

With the explosive growth of social network, the exploitation of social information in recommendation models has become increasingly significant. However, most existing models only made use of users’ social information, ignoring the value of items’ social information. Based on the above fact, we present a listwise learning to rank recommendation model, UIContextListRank, which generates a ranked list of items for individual users directly. We employ matrix factorization to construct a listwise objective function that measures the difference between the predicted lists and the real ones. Furthermore, we express users’ social contextual information as their trust friends and items’ social contextual information as their concurrent items, and incorporate the social contextual information of both users’ and items’ into the listwise model to improve recommendation quality. Moreover, we implement our proposed model in a distributed environment to tackle the challenge of overwhelming data. Experiments have been conducted on two real-world datasets to evaluate the proposed model. And the experimental results prove the model’s effectiveness and efficiency.