Search Results for author: Beibei Kong

Found 5 papers, 3 papers with code

OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities

no code implementations23 May 2023 Yuanzhen Xie, Tao Xie, Mingxiong Lin, WenTao Wei, Chenglin Li, Beibei Kong, Lei Chen, Chengxiang Zhuo, Bo Hu, Zang Li

At present, most approaches focus on chains of thought (COT) and tool use, without considering the adoption and application of human cognitive frameworks.

Active Learning Decision Making +1

Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

2 code implementations13 Oct 2022 Guanghu Yuan, Fajie Yuan, Yudong Li, Beibei Kong, Shujie Li, Lei Chen, Min Yang, Chenyun Yu, Bo Hu, Zang Li, Yu Xu, XiaoHu Qie

Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback.

Recommendation Systems

TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

no code implementations13 Jun 2022 Jie Wang, Fajie Yuan, Mingyue Cheng, Joemon M. Jose, Chenyun Yu, Beibei Kong, Xiangnan He, Zhijin Wang, Bo Hu, Zang Li

That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms.

Recommendation Systems Transfer Learning

RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation

1 code implementation19 Nov 2021 Chenglin Li, Mingjun Zhao, Huanming Zhang, Chenyun Yu, Lei Cheng, Guoqiang Shu, Beibei Kong, Di Niu

The learned GUR captures the overall preferences and characteristics of a user and thus can be used to augment the behavior data and improve recommendations in any single domain in which the user is involved.

Sequential Recommendation

One Person, One Model, One World: Learning Continual User Representation without Forgetting

2 code implementations29 Sep 2020 Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou, Joemon Jose, Beibei Kong, Yudong Li

In this paper, we delve on research to continually learn user representations task by task, whereby new tasks are learned while using partial parameters from old ones.

Recommendation Systems

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