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Selection bias mitigation in recommender system using uninteresting items based on temporal visibility
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In the citing paper
“Additionally, because users tend to give explicit feedback only on items they feel strongly about, either positively or negatively, the user-item interaction matrix becomes sparse, and the missing values in the matrix are not random [9].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [9]
In the citing paper
“As a result, implicit feedback is not always a reliable indicator of a user's valid preferences [9].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [9]
In the citing paper
“Among these, CF-based approaches are more widely used [9, 13-16].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [9]
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Research works printed but not linked: 17
- Ashokan, 2021, Fairness metrics and bias mitigation strategies for rating predictions, Information Processing & Management, 58, 10.1016/j.ipm.2021.102646 · DOI · Paper page
- Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2020). Bias and debias in recommender system: A survey and future directions. arXiv preprint arXiv:2010.03240. · arXiv
- Cheng, 2019, Collaborative Filtering Algorithm based on Data Mixing and Filtering, International Journal of Performability Engineering, 15, 2267, 10.23940/ijpe.19.08.p27.22672276 · DOI
- Chen, 2021, A movie recommendation method based on users' positive and negative profiles, Information Processing & Management, 58, 10.1016/j.ipm.2021.102531 · DOI
- Deldjoo, 2021, Explaining recommender systems fairness and accuracy through the lens of data characteristics, Information Processing & Management, 58, 10.1016/j.ipm.2021.102662 · DOI
- Elahi, 2021, Investigating the impact of recommender systems on user-based and item-based popularity bias, Information Processing & Management, 58, 10.1016/j.ipm.2021.102655 · DOI
- He, R., & McAuley, J. (2016, February). VBPR: visual bayesian personalized ranking from implicit feedback. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 30, No. 1). · DOI
- Ha, 2017, Item-network-based collaborative filtering: A personalized recommendation method based on a user's item network, Information Processing & Management, 53, 1171, 10.1016/j.ipm.2017.05.003 · DOI
- Koren, 2009, Matrix factorization techniques for recommender systems, Computer, 42, 30, 10.1109/MC.2009.263 · DOI
- Lu, 2015, Recommender system application developments: A survey, Decision Support Systems, 74, 12, 10.1016/j.dss.2015.03.008 · DOI
- Misztal-Radecka, 2021, Bias-Aware Hierarchical Clustering for detecting the discriminated groups of users in recommendation systems, Information Processing & Management, 58, 10.1016/j.ipm.2021.102519 · DOI · Paper page
- Nguyen, 2022, High-order autoencoder with data augmentation for collaborative filtering, Knowledge-Based Systems, 240, 10.1016/j.knosys.2021.107773 · DOI
- Pan, W., Cui, S., Wen, H., Chen, K., Zhang, C., & Wang, F. (2021). Correcting the User Feedback-Loop Bias for Recommendation Systems. arXiv preprint arXiv:2109.06037. · arXiv
- Sarkar, 2020, Implicit information need as explicit problems, help, and behavioral signals, Information Processing & Management, 57, 10.1016/j.ipm.2019.102069 · DOI
- Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618. · arXiv
- Xue, 2017, August). Deep Matrix Factorization Models for Recommender Systems, In IJCAI, 17, 3203
- Yuan, 2021, Preliminary data-based matrix factorization approach for recommendation, Information Processing & Management, 58, 10.1016/j.ipm.2020.102384 · DOI
Cited by
Sorted by citations
Roy, F., Ding, X., Choo, K. K. R., & Zhou, P. (2024) · arXiv · doi:10.48550/arxiv.2409.12651 · Cited by 7
Self-citation
Metadata
- Authors
- Shi, L., Li, S., Ding, X., & Bu, Z.
- Affiliations
- Nanjing University of Finance and Economics (CN)
- Nanjing University (CN)
- Corresponding author
- Li, Shuqing
- Ding, Xiaowei
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 213
- Pages
- 118932
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W4298148589
- Cited by
- 14
- References
- 0
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 6 · 2023: 3
- FWCI
- 2.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- selection bias, collaborative filtering, data gap filling, explicit feedback, top-N recommendation, user activity, item popularity
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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- CoChat. (2026). Citation check: Selection bias mitigation in recommender system using uninteresting items based on temporal visibility. https://app.cochat.ai/papers/10.1016/j.eswa.2022.118932
- The paper
- Shi, L., Li, S., Ding, X., & Bu, Z. (2023). Selection bias mitigation in recommender system using uninteresting items based on temporal visibility. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2022.118932
