Zhang, Jizhi

TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

Bao, K., Zhang, J., Zhang, Y., Wang, W., Feng, F., & He, X. (2023) · Proceedings of the 17th ACM Conference on Recommender Systems

Recommender Systems and TechniquesTopic Modeling

Cited by 469

Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation

Zhang, J., Bao, K., Zhang, Y., Wang, W., Feng, F., & He, X. (2023) · arXiv

Ethics and Social Impacts of AIRecommender Systems and Techniques

Cited by 196

CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation

Zhang, Y., Feng, F., Zhang, J., Bao, K., Wang, Q., & He, X. (2023) · arXiv

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 146

Large Language Models are Learnable Planners for Long-Term Recommendation

Shi, W., He, X., Zhang, Y., Gao, C., Li, X., Zhang, J., Wang, Q., & Feng, F. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Recommender Systems and TechniquesAdvanced Bandit Algorithms Research

Cited by 35

Decoding Matters: Addressing Amplification Bias and Homogeneity Issue in Recommendations for Large Language Models

Bao, K., Zhang, J., Zhang, Y., Huo, X., Chen, C., & Feng, F. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 23

Agentic Feedback Loop Modeling Improves Recommendation and User Simulation

Cai, S., Zhang, J., Bao, K., Gao, C., Wang, Q., Feng, F., & He, X. (2024) · arXiv

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 14