Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment

Xin, X. et al. (2023) Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment. In: 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR23), Taipei, Taiwan, 23-27 July 2023, pp. 932-941. ISBN 9781450394086 (doi: 10.1145/3539618.3591697)

Abstract

Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases. Using multiple types of implicit user feedback for such target behavior prediction purposes is still an open question. Existing studies that attempted to learn from multiple types of user behavior often fail to: (i) learn universal and accurate user preferences from different behavioral data distributions, and (ii) overcome the noise and bias in observed implicit user feedback. To address the above problems, we propose multi-behavior alignment (MBA), a novel recommendation framework that learns from implicit feedback by using multiple types of behavioral data. We conjecture that multiple types of behavior from the same user (e.g., clicks and purchases) should reflect similar preferences of that user. To this end, we regard the underlying universal user preferences as a latent variable. The variable is inferred by maximizing the likelihood of multiple observed behavioral data distributions and, at the same time, minimizing the Kullback?Leibler divergence (KL-divergence) between user models learned from auxiliary behavior (such as clicks or views) and the target behavior separately. MBA infers universal user preferences from multi-behavior data and performs data denoising to enable effective knowledge transfer. We conduct experiments on three datasets, including a dataset collected from an operational e-commerce platform. Empirical results demonstrate the effectiveness of our proposed method in utilizing multiple types of behavioral data to enhance the prediction of the target behavior.

• Item Type: Conference Proceedings
• Additional Information: This research was funded by the Natural Science Foundation of China (62272274,61972234,62072279,62102234,62202271), Meituan, the Natural Science Foundation of Shandong Province (ZR2022QF004), the Key Scientific and Technological Innovation Program of Shandong Province (2019JZZY010129), Shandong University multidisciplinary research and innovation team of young scholars (No.2020QNQT017), the Tencent WeChat Rhino-Bird Focused Research Program (JR-WXG2021411), the Fundamental Research Funds of Shandong University, the Hybrid Intelligence Center, a 10-year program funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organization for Scientific Research, https://hybrid-intelligence-centre.nl.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jose, Professor Joemon
• Authors: Xin, X., Liu, X., Wang, H., Ren, P., Chen, Z., Lei, J., Shi, X., Luo, H., Jose, J. M., de Rijke, M., and Ren, Z.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781450394086
• Copyright Holders: Copyright © 2023 The Author(s)
• First Published: First published in SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval: 932–941
• Publisher Policy: Reproduced in accordance with the publisher copyright policy