Improving Sequential Recommenders through Counterfactual Augmentation of System Exposure
In sequential recommendation, system exposure refers to items that are exposed to the user. Typically, the user only interactions with a few of the exposed items. Although sequential recommendation has achieved great success in predicting future user interests, existing sequential recommendation methods do not fully exploit system exposure data. Most methods only model items that have been interacted with, while the large volume of exposed but non-interacted items is overlooked. Even methods that consider system exposure typically train the recommender using only the logged historical system exposure, without exploring unseen user interests.
Authors
- Maarten de Rijke (ORCID: https://orcid.org/0000-0002-1086-0202)
- Pengjie Ren (ORCID: https://orcid.org/0000-0003-2964-6422)
- Zhumin Chen (ORCID: https://orcid.org/0000-0003-4592-4074)
- Zihan Wang (ORCID: https://orcid.org/0000-0003-0493-2668)
- Zhaochun Ren (ORCID: https://orcid.org/0000-0002-9076-6565)
- Xin Xin (ORCID: https://orcid.org/0000-0001-6116-9115)
- Ziqi Zhao (ORCID: https://orcid.org/0009-0008-3011-5745)
- Liu Yang (ORCID: https://orcid.org/0009-0007-7508-0964)
- Jiyuan Yang (ORCID: https://orcid.org/0000-0003-2700-5533)
- Zhengjun Yan
Institutions
- Leiden University (NL)
- Shandong University (CN)
- Amsterdam University of the Arts (NL)
- University of Amsterdam (NL)
Publication Details
- Published
- 2025-07-13
- DOI
- https://doi.org/10.1145/3726302.3730005
- Primary Topic
- Recommender Systems and Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00