Search Results for author: Chenglei Shen

Found 5 papers, 0 papers with code

Generating Model Parameters for Controlling: Parameter Diffusion for Controllable Multi-Task Recommendation

no code implementations14 Oct 2024 Chenglei Shen, Jiahao Zhao, Xiao Zhang, Weijie Yu, Ming He, Jianping Fan

To address this issue, we propose a novel controllable learning approach via Parameter Diffusion for controllable multi-task Recommendation (PaDiRec), which allows the customization and adaptation of recommendation model parameters to new task requirements without retraining.

Recommendation Systems Test-time Adaptation

Enhancing Sequential Recommendations through Multi-Perspective Reflections and Iteration

no code implementations10 Sep 2024 Weicong Qin, Yi Xu, Weijie Yu, Chenglei Shen, Xiao Zhang, Ming He, Jianping Fan, Jun Xu

Specifically, MoRE introduces three reflectors for generating LLM-based reflections on explicit preferences, implicit preferences, and collaborative signals.

Collaborative Filtering

A Survey of Controllable Learning: Methods and Applications in Information Retrieval

no code implementations4 Jul 2024 Chenglei Shen, Xiao Zhang, Teng Shi, Changshuo Zhang, Guofu Xie, Jun Xu

Controllability has become a crucial aspect of trustworthy machine learning, enabling learners to meet predefined targets and adapt dynamically at test time without requiring retraining as the targets shift.

Information Retrieval Retrieval

On the Decision-Making Abilities in Role-Playing using Large Language Models

no code implementations29 Feb 2024 Chenglei Shen, Guofu Xie, Xiao Zhang, Jun Xu

Large language models (LLMs) are now increasingly utilized for role-playing tasks, especially in impersonating domain-specific experts, primarily through role-playing prompts.

Decision Making

HyperBandit: Contextual Bandit with Hypernewtork for Time-Varying User Preferences in Streaming Recommendation

no code implementations14 Aug 2023 Chenglei Shen, Xiao Zhang, Wei Wei, Jun Xu

In real-world streaming recommender systems, user preferences often dynamically change over time (e. g., a user may have different preferences during weekdays and weekends).

Recommendation Systems

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