Causal Inference in Recommender Systems
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 15 April 2025 | Viewed by 2245
Special Issue Editors
Interests: recommendation; information retrieval; causal inference; large language model; natural language processing
Special Issue Information
Dear Colleagues,
The recommender system serves many users with personalized information filtering across a wide spectrum of online applications such as e-commerce, search engines, and social media. Recent years have witnessed the success of incorporating causal inference theories and techniques into recommender systems to enhance the user experience regarding the accuracy of user preference modeling and estimation, as well as the fairness, unbiasedness, and transparency of recommendations. In addition, these recommender systems also draw upon concepts from entropy and information theory. The connection between these directions indicates opportunities to futher improve the performance of recommender systems. For example, recommender systems can better understand and predict user behavior by considering the entropy of user preferences and the information gain obtained through causal inference models. This Special Issue is aimed at bringing together the most contemporary achievements and breakthroughs in the field of recommender systems that embrace causal inference and information theory. We invite novel contributions on topics including, but not restricted to, the following:
- Causal view of recommender system;
- Causal user modeling;
- Causal effect estimation for recommendation;
- Bias and debias in recommender system;
- Causal representation learning;
- Counterfactual learning for recommendation;
- Uncertainty of recommendation;
- Information decomposition for user modeling;
- Causal discovery in recommender system;
- Causal explanation for recommendation;
- Causal evaluation of recommender system;
- Causal tools and resources of recommender system;
- Unmeasured confounder modeling based on information theory;
- Debiased recommendation based on information theory.
Technical Committee Member
Name: Haoxuan Li
Email: [email protected]
Affiliation: Center for Data Science, Peking University, Beijing 100091, China
Interests: causal inference; recommendation; selection bias; fairness; large language model
Website: https://pattern.swarma.org/user/62913
Prof. Dr. Fuli Feng
Dr. Xu Chen
Guest Editors
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Keywords
- recommendation
- causal inference
- inference retrieval
- ranking
- information theory
- information bottleneck
- representation learning
- counterfactual explanation
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