Fuzzy Collaborative Reasoning

Abstract

Collaborative reasoning enhances recommendation performance by combining the strengths of symbolic learning and deep neural learning. However, current collaborative reasoning models rely on parameterized networks to simulate logical operations within the reasoning process, which (1) do not comply with all axiomatic principles of classical logic and (2) limit the model's generalizability. To address these limitations, a Fuzzy logic approach tailored for Collaborative Reasoning (FuzzCR) is proposed in this work, aiming to augment the recommendation system with cognitive abilities. Specifically, this method redefines the sequential recommendation task as a logical query answering process to facilitate a more structured and logical progression of reasoning. Moreover, learning-free fuzzy logical operations are implemented for the designed reasoning process. Taking advantage of the inherent properties of fuzzy logic, these logical operations satisfy fundamental logical rules and ensure complete reasoning. After training, these operations can be applied to flexible reasoning processes, rather than being confined to fixed computation graphs, thereby exhibiting good generalizability. Extensive experiments conducted on publicly available datasets demonstrate the superiority of this method in solving the sequential recommendation task.