Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering

• URL: http://arxiv.org/abs/2311.06777v1
• Date: Sun, 12 Nov 2023 08:46:07 GMT
• Title: Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
• Authors: Yijie Zhang, Yuanchen Bei, Shiqi Yang, Hao Chen, Zhiqing Li, Lijia Chen, Feiran Huang
• Abstract summary: We propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering. IMGCF utilizes a multi-task learning framework for collaborative filtering on multi-behavior graphs. Experiments on two widely-used multi-behavior datasets demonstrate the effectiveness of IMGCF.
• Score: 14.396131602165598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph collaborative filtering, which learns user and item representations through message propagation over the user-item interaction graph, has been shown to effectively enhance recommendation performance. However, most current graph collaborative filtering models mainly construct the interaction graph on a single behavior domain (e.g. click), even though users exhibit various types of behaviors on real-world platforms, including actions like click, cart, and purchase. Furthermore, due to variations in user engagement, there exists an imbalance in the scale of different types of behaviors. For instance, users may click and view multiple items but only make selective purchases from a small subset of them. How to alleviate the behavior imbalance problem and utilize information from the multiple behavior graphs concurrently to improve the target behavior conversion (e.g. purchase) remains underexplored. To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering. Specifically, IMGCF utilizes a multi-task learning framework for collaborative filtering on multi-behavior graphs. Then, to mitigate the data imbalance issue, IMGCF improves representation learning on the sparse behavior by leveraging representations learned from the behavior domain with abundant data volumes. Experiments on two widely-used multi-behavior datasets demonstrate the effectiveness of IMGCF.

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