Compressed Interaction Graph based Framework for Multi-behavior Recommendation
| Autor: | Guo, Wei, Meng, Chang, Yuan, Enming, He, Zhicheng, Guo, Huifeng, Zhang, Yingxue, Chen, Bo, Hu, Yaochen, Tang, Ruiming, Li, Xiu, Zhang, Rui |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Wei Guo and Chang Meng are co-first authors and contributed equally to this research. Chang Meng is supervised by Wei Guo when he was a research intern at Huawei Noah's Ark Lab |
| Databáze: | arXiv |
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