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Treatment Effect Estimation for User Interest Exploration on Recommender Systems
Published in SIGIR 2024 (The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval), 2024
An uplift model-based framework that optimizes category exposure in recommendations through causal treatment effect estimation, effectively discovering hidden user interests while maximizing engagement.
Recommended citation: Chen, J., Wenjie, W., Gao, C., Wu, P., Wei, J., & Hua, Q. (2024, July). Treatment Effect Estimation for User Interest Exploration on Recommender Systems. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1861-1871).
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DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems
Published in WSDM 2025 (The 18th ACM International Conference on Web Search and Data Mining), 2025
A fine-grained controllable recommendation framework that enables precise diversity management in LLM-based systems through task decomposition and robust data augmentation techniques.
Recommended citation: Chen, J., Gao, C., Yuan, S., Liu, S., Cai, Q., & Jiang, P. (2024). DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender Systems. arXiv preprint arXiv:2408.12470.
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