Neuro-symbolic recommendation model based on logic query

Abstract

Recommendation systems assist users in finding items that are relevant to them. However, recommendation is not only an inductive statistics problem using data; it is also a cognitive task of reasoning decisions based on knowledge extracted from information. Hence, a logic system can naturally be incorporated for reasoning in a recommendation task. Although logic-based hard-rule approaches can provide a powerful reasoning ability, they struggle to cope with inconsistent and incomplete knowledge in real-world tasks, especially for complex tasks such as recommendation. To address these issues, we propose a neuro-symbolic recommendation model, which transforms user history interactions into a logic expression and then transforms the recommendation prediction into a query task based on this logic expression. The logic expressions are then computed based on the modular logic operations of the neural network. We also construct an implicit logic encoder to reasonably reduce the complexity of the logic computation. Finally, a user’s interest items can be queried in the vector space based on the computation results. Experiments on three well-known datasets verified that the proposed method outperforms state of the art shallow, deep, session, and reasoning models.