Compressed Interaction Graph based Framework for Multi-behavior Recommendation

• URL: http://arxiv.org/abs/2303.02418v1
• Date: Sat, 4 Mar 2023 13:41:36 GMT
• Title: Compressed Interaction Graph based Framework for Multi-behavior Recommendation
• Authors: Wei Guo, Chang Meng, Enming Yuan, Zhicheng He, Huifeng Guo, Yingxue Zhang, Bo Chen, Yaochen Hu, Ruiming Tang, Xiu Li, Rui Zhang
• Abstract summary: It is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior. We propose CIGF, a Compressed Interaction Graph based Framework, to overcome the above limitations. We propose a Multi-Expert with Separate Input (MESI) network with separate input on the top of CIGCN for multi-task learning.
• Score: 46.16750419508853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-faceted preferences. However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''. In this paper, we propose CIGF, a Compressed Interaction Graph based Framework, to overcome the above limitations. Specifically, we design a novel Compressed Interaction Graph Convolution Network (CIGCN) to model instance-level high-order relations explicitly. To alleviate the potential gradient conflict when treating multi-behavior data ''as labels'', we propose a Multi-Expert with Separate Input (MESI) network with separate input on the top of CIGCN for multi-task learning. Comprehensive experiments on three large-scale real-world datasets demonstrate the superiority of CIGF. Ablation studies and in-depth analysis further validate the effectiveness of our proposed model in capturing high-order relations and alleviating gradient conflict. The source code and datasets are available at https://github.com/MC-CV/CIGF.

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