Temporal Graph Neural Networks for Social Recommendation

In social recommendation, the purchase decision of users is influenced by their basic preference of items, as well as the social influence of peers. Such social connections had been proved to be effective in modeling users' preference of items. However, most models in social recommender literature only considered two types of relations, i.e., user-item relation in interaction network and user-user relation in social network. The temporal sequential information of items, i.e., item-item relation, can also be utilized to infer the preference of users, but had been ignored in almost all of the graph based recommendation models. Two issues of such temporal information had not been well studied in social recommender systems: the temporal strength information, i.e., the real purchase time of an item, and its influence on social relations. To address the above issues, we propose a novel Temporal Enhanced Graph Model for Social Recommendation (TGRec). In TGRec, the purchase time information between items is characterized as a special temporal relation, and the purchase decision of users depends on three factors: (1) a user's basic preference of items, (2) the collaborative influence of peers, (3) the temporal impact of previous items bought by the user. Experimental results on three real-world commerce datasets demonstrate the effectiveness of our model for social recommendation, showing the usefulness of modeling the temporal information in heterogeneous graph.