Enhanced Graph Learning for Recommendation via Causal Inference

Suhua Wang, Hongjie Ji, Minghao Yin, Yuling Wang, Mengzhu Lu and Hui Sun
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Suhua Wang: Computer Department, Changchun Humanities and Sciences College, Changchun 130117, China
Hongjie Ji: School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
Minghao Yin: School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
Yuling Wang: School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
Mengzhu Lu: School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
Hui Sun: Computer Department, Changchun Humanities and Sciences College, Changchun 130117, China

Mathematics, 2022, vol. 10, issue 11, 1-20

Abstract: The goal of the recommender system is to learn the user’s preferences from the entity (user–item) historical interaction data, so as to predict the user’s ratings on new items or recommend new item sequences to users. There are two major challenges: (1) Datasets are usually sparse. The item side is often accompanied by some auxiliary information, such as attributes or context; it can help to slightly improve its representation. However, the user side is usually presented in the form of ID due to personal privacy. (2) Due to the influences of confounding factors, such as the popularity of items, users’ ratings on items often have bias that cannot be recognized by the traditional recommendation methods. In order to solve these two problems, in this paper, (1) we explore the use of a graph model to fuse the interactions between users and common rating items, that is, incorporating the “neighbor” information into the target user to enrich user representations; (2) the d o ( ) operator is used to deduce the causality after removing the influences of confounding factors, rather than the correlation of the data surface fitted by traditional machine learning. We propose the EGCI model, i.e., enhanced graph learning for recommendation via causal inference. The model embeds user relationships and item attributes into the latent semantic space to obtain high-quality user and item representations. In addition, the mixed bias implied in the rating process is calibrated by considering the popularity of items. Experimental results on three real-world datasets show that EGCI is significantly better than the baselines.

Keywords: causal inference; enhanced graph learning; do () operation; graph model; recommender system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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