DIAG: A Deep Interaction-Attribute-Generation model for user-generated item recommendation

Most existing recommendation methods assume that all the items are provided by separate producers rather than users. However, it could be inappropriate in some recommendation tasks since users may generate some items. Considering the user–item generation relation may benefit recommender systems that only use implicit user–item interactions. However, it may suffer from a dramatic imbalance. The number of user–item generation relations may be far smaller than the number of user–item interactions because each item is generated by at most one user. At the same time, this item can be interacted with by many users. To overcome the challenging imbalance issue, we propose a novel Deep Interaction-Attribute-Generation (DIAG) model. It integrates the user–item interaction relation, the user–item generation relation, and the item attribute information into one deep learning framework. The novelty lies in the design of a new item–item co-generation network for modeling the user–item generation information. Then, graph attention network is adopted to learn the item feature vectors from the user–item generations and the item attribute information by considering the adaptive impact of one item on its co-generated items. Extensive experiments conducted on two real-world datasets confirm the superiority of the DIAG method.