Deconfounding User Preference in Recommendation Systems through Implicit and Explicit Feedback
Authors: 
Yuliang Liang, Enneng Yang, Guibing Guo, Wei Cai, Linying Jiang, Xingwei WangAuthors Info & Claims
Yuliang Liang, Enneng Yang, Guibing Guo, Wei Cai, Linying Jiang, Xingwei WangAuthors Info & ClaimsArticle No.: 198, Pages 1 - 18
Abstract
Recommender systems are influenced by many confounding factors (i.e., confounders) which result in various biases (e.g., popularity biases) and inaccurate user preference. Existing approaches try to eliminate these biases by inference with causal graphs. However, they assume all confounding factors can be observed and no hidden confounders exist. We argue that many confounding factors (e.g., season) may not be observable from user–item interaction data, resulting inaccurate user preference. In this article, we propose a deconfounded recommender considering unobservable confounders. Specifically, we propose a new causal graph with explicit and implicit feedback, which can better model user preference. Then, we realize a deconfounded estimator by the front-door adjustment, which is able to eliminate the effect of unobserved confounders. Finally, we conduct a series of experiments on two real-world datasets, and the results show that our approach performs better than other counterparts in terms of recommendation accuracy.
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Publication History
Published: 21 August 2024
Online AM: 18 June 2024
Accepted: 10 June 2024
Revised: 15 May 2024
Received: 09 July 2023
Published in TKDD Volume 18, Issue 8
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- National Natural Science Foundation of China
- Science and technology projects in Liaoning Province
- Fundamental Research Funds for the Central Universities
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