Proactive Recommendation with Iterative Preference Guidance
Author:
Affiliation:
1. University of Science and Technology of China, Hefei, China
2. National University of Singapore, Singapore, Singapore
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589335.3651548
Reference14 articles.
1. Keqin Bao Jizhi Zhang Yang Zhang Wenjie Wang Fuli Feng and Xiangnan He. 2023. TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. In RecSys. ACM 1007--1014.
2. Wei Cai Fuli Feng Qifan Wang Tian Yang Zhenguang Liu and Congfu Xu. 2023. A Causal View for Item-level Effect of Recommendation on User Preference. In WSDM. 240--248.
3. Estimating and Penalizing Induced Preference Shifts in Recommender Systems;Carroll Micah D.;ICML,2022
4. Dan Cosley, Shyong K. Lam, Istvá n Albert, Joseph A. Konstan, and John Riedl. 2003. Is seeing believing?: how recommender system interfaces affect users' opinions. In CHI. 585--592.
5. Mihaela Curmei Andreas A. Haupt Benjamin Recht and Dylan Hadfield-Menell. 2022. Towards Psychologically-Grounded Dynamic Preference Models. In RecSys. 35--48.
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