CausPref: Causal Preference Learning for Out-of-Distribution Recommendation
Open accessAbstract
In spite of the tremendous development of recommender system owing to the progressive capability of machine learning recently, the current recommender system is still vulnerable to the distribution shift of users and items in realistic scenarios, leading to the sharp decline of performance in testing environments. It is even more severe in many common applications where only the implicit feedback from sparse data is available. Hence, it is crucial to promote the performance stability of recommendation method in different environments. In this work, we first make a thorough analysis of implicit recommendation problem from the viewpoint of out-of-distribution (OOD) generalization. Then under the guidance of our theoretical analysis, we propose to incorporate the recommendation-specific DAG learner into a novel causal preference-based recommendation framework named CausPref, mainly consisting of causal learning of invariant user preference and anti-preference negative sampling to deal with implicit feedback. Extensive experimental results from real-world datasets clearly demonstrate that our approach surpasses the benchmark models significantly under types of out-of-distribution settings, and show its impressive interpretability.
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When highly-used items are incorporated into new training datasets, information retrieval systems tend to rank them higher in subsequent iterations, resulting in the open access citation advantage.
“When these highly-used items are incorporated into new training datasets, information retrieval systems tend to rank them higher in subsequent iterations, resulting in the open access citation advantage[13].”
From Generative Agents Navigating Digital Libraries (2025) · cites this paper as [13]
The Introduction (page unavailable) says popular items may have more chances to be exposed, and §4 EXPERIMENTS says unpopular items receive less exposure. The paper does not establish that incorporating highly used items into later training datasets raises their rankings, or that this produces an open-access citation advantage. (LLM, conf=0.82)
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Cited by
Sorted by citations
Generative Agents Navigating Digital Libraries
Zerhoudi, S., & Granitzer, M. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-0865-2_14 · Cited by 1 · see citing works
- Authors
- He, Y., Wang, Z., Cui, P., Zou, H., Zhang, Y., Cui, Q., & Jiang, Y.
- Affiliations
- Tsinghua University (CN)
- Meituan (China) (CN)
- Venue
- Proceedings of the ACM Web Conference 2022
- Publisher
- ACM
- Published
- 2022-04-25
- Pages
- 410-421
- OpenAlex
- W4220806694
- Cited by
- 50
- References
- 46
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 24 · 2023: 10 · 2022: 1
- FWCI
- 10.28
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (No. U1936219, 62141607, 62141607, U1936219)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- implicit feedback, distribution shift, data sparsity
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