LightFIG: simplifying and powering feature interactions via graph for recommendation

The attributes of users and items contain key information for recommendation. The latest advances demonstrate that better representations can be learned by performing graph convolutions on attribute graph of the user-item pair. Recently proposed models construct graphs that not only connect edges between user attributes and item attributes, but also within user (item) attributes. However, to determine whether a user is interested in an item, the relationships between user attributes and item attributes are what we really need to mine. In many cases, due to the low correlation between relationships within user (item) attributes and preference of the user, the artificially connected edges within user (item) attributes contribute little to the recommendation. Even worse, including them will not only drastically increase the training time, but may also introduce noise and lead to degraded performance. In addition, the use of the optimizer is also relatively simple. One single optimizer is the default configuration for recommendation models. This may not be the best way to exploit it in many cases however. To solve these problems, we propose an enhanced model named LightFIG in this work. The key idea of LightFIG is twofold: First, we simplify the construction of attribute graph which focuses on mining relationships cross user attributes and item attributes, not between user (item) attributes. Second, we propose the idea of relay optimization, which employs two different optimizers to continuously optimize model parameters. Comprehensive experiments on three public datasets demonstrate the effectiveness of our proposed model.