A recommendation system assists users in finding items that are relevant to
them. Existing recommendation models are primarily based on predicting
relationships between users and items and use complex matching models or
incorporate extensive external information to capture association patterns in
data. However, recommendation is not only a problem of inductive statistics
using data; it is also a cognitive task of reasoning decisions based on
knowledge extracted from information. Hence, a logic system could naturally be
incorporated for the reasoning in a recommendation task. However, although
hard-rule approaches based on logic systems can provide powerful reasoning
ability, they struggle to cope with inconsistent and incomplete knowledge in
real-world tasks, especially for complex tasks such as recommendation.
Therefore, in this paper, we propose a neuro-symbolic recommendation model,
which transforms the 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 our method
performs better compared to state of the art shallow, deep, session, and
reasoning models.Comment: 17 pages, 6 figure