CoCNN: Co-occurrence CNN for recommendation

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.

Automated summary

In collaborative filtering, we propose a novel neural network, CoCNN, which combines co-occurrence patterns and CNN to improve performance using implicit feedback. The co-occurrence pattern identifies items that consistently appear between pairs on a user's favorite list. By establishing co-occurrence relationships, we successfully capture more useful information.