Knowledge Enhancement for Multi-Behavior Contrastive Recommendation

• URL: http://arxiv.org/abs/2301.05403v1
• Date: Fri, 13 Jan 2023 06:24:33 GMT
• Title: Knowledge Enhancement for Multi-Behavior Contrastive Recommendation
• Authors: Hongrui Xuan, Yi Liu, Bohan Li, Hongzhi Yin
• Abstract summary: We propose a Knowledge Enhancement Multi-Behavior Contrastive Learning Recommendation (KMCLR) framework. In this work, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement. In the optimization stage, we model the coarse-grained commonalities and the fine-grained differences between multi-behavior of users to further improve the recommendation effect.
• Score: 39.50243004656453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A well-designed recommender system can accurately capture the attributes of users and items, reflecting the unique preferences of individuals. Traditional recommendation techniques usually focus on modeling the singular type of behaviors between users and items. However, in many practical recommendation scenarios (e.g., social media, e-commerce), there exist multi-typed interactive behaviors in user-item relationships, such as click, tag-as-favorite, and purchase in online shopping platforms. Thus, how to make full use of multi-behavior information for recommendation is of great importance to the existing system, which presents challenges in two aspects that need to be explored: (1) Utilizing users' personalized preferences to capture multi-behavioral dependencies; (2) Dealing with the insufficient recommendation caused by sparse supervision signal for target behavior. In this work, we propose a Knowledge Enhancement Multi-Behavior Contrastive Learning Recommendation (KMCLR) framework, including two Contrastive Learning tasks and three functional modules to tackle the above challenges, respectively. In particular, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement, and utilize knowledge graph in the knowledge enhancement module to derive more robust knowledge-aware representations for items. In addition, in the optimization stage, we model the coarse-grained commonalities and the fine-grained differences between multi-behavior of users to further improve the recommendation effect. Extensive experiments and ablation tests on the three real-world datasets indicate our KMCLR outperforms various state-of-the-art recommendation methods and verify the effectiveness of our method.

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