Unified User and Item Representation Learning for Joint Recommendation in Social Network

Friend and item recommendation in online social networks is a vital task, which benefits for both users and platform providers. However, extreme sparsity of user-user matrix and user-item matrix issue create severe challenges, causing collaborative filtering methods to degrade significantly in their recommendation performance. Moreover, the factors those affect users' preference for items and friends are complex in social networks. For example, users may be influenced by their friends in addition to their own preferences when they choose items. To tackle these problems, we first construct two implicit graphs of users according to the users' shared neighbours in friendship network and the users' common interested items in interest network to ease data sparsity issue. Then we stand on recent advances in embedding learning techniques and propose a unified graph-based embedding model, called UGE. UGE learns two implicit representations for each user from implicit graphs, so that users can be represented as two weighted implicit representations which reflect the influence of friendship and interest. The weights and items' representation can be learnt from explicit friendship network and interest network mutually. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach.