CoCNN: Co-occurrence CNN for recommendation

Abstract

Under the assumption that items are independent and identically distributed, most existing CF methods learn representation from user–item pairs, but ignore the connections among items, leading to limited performance. Considering the challenge of recommendation, we propose a novel neural network, CoCNN, which combines a Co-occurrence pattern and CNN for CF with implicit feedback. The key idea of the co-occurrence pattern is that some items always appear between pairs on a user’s favorite list. In CoCNN, co-occurrence relationships act as a bridge in user–item pairs and item–item pairs, which are not observed directly. To model user–item and item–item information simultaneously, we propose a multi-task neural network to share the knowledge of the two tasks. Finally, experimental results demonstrate that CoCNN successfully captures more useful information, and therefore can be used as a simple and effective tool for recommendation. Our projects are available online at https://github.com/XiuzeZhou/CoCNN.