CoNet: Co-occurrence neural networks for recommendation

Abstract Assuming that both users and items are independent and identically distributed, most existing methods model user–item pairs, while ignoring the relationship between items, leading to limited performance. To solve this problem, we propose a novel neural network, CoNet, which can effectively model the co-occurrence pattern for Collaborative Filtering (CF). We argue that items always occur in pairs, i.e. an item co-occurrence pattern. For example, movies ”Harry Potter 1” and ”Harry Potter 2” are always viewed by users who like magic style films. To learn the latent features, CoNet is simultaneously modeled on user–item and item–item interactions. Compared with methods that train on a single user–item pair, CoNet can encode highly descriptive features from the co-occurrence pattern. To achieve a better performance, we design an attention network to learn the weight of a user’s preference for different items and subsequently aggregate the weighted embeddings to obtain the co-occurrence representations. Finally, we conducted extensive experiments using several data sets, which show that the proposed method is superior to other baseline approaches. Source code of CoNet is available from https://github.com/XiuzeZhou/conet .