1 code implementation • 9 Nov 2024 • Jiayin Wang, XiaoYu Zhang, Weizhi Ma, Min Zhang
Firstly, we train an autoencoder with sparsity constraints to reconstruct internal activations of recommendation models, making the RecSAE latents more interpretable and monosemantic than the original neuron activations.
1 code implementation • 1 Nov 2024 • Chumeng Jiang, Jiayin Wang, Weizhi Ma, Charles L. A. Clarke, Shuai Wang, Chuhan Wu, Min Zhang
We intend our evaluation framework and observations to benefit future research on the use of LLMs as recommenders.
1 code implementation • 29 Oct 2024 • Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo, Weizhi Ma, Min Zhang
The online retrieval part follows the paradigm of relevant recall and personalized ranking, supported by the offline pre-calculation parts, which are sticker semantic understanding, utility evaluation and personalization modules.
no code implementations • 27 Oct 2024 • Zihan Wang, Xuri Ge, Joemon M. Jose, HaiTao Yu, Weizhi Ma, Zhaochun Ren, Xin Xin
At the end of the workshop, we aim to have a clearer understanding of how to improve the reliability and applicability of RAG with more robust information retrieval and language generation.
no code implementations • 21 Oct 2024 • Xun Jiang, Feng Li, Han Zhao, Jiaying Wang, Jun Shao, Shihao Xu, Shu Zhang, Weiling Chen, Xavier Tang, Yize Chen, Mengyue Wu, Weizhi Ma, Mengdi Wang, Tianqiao Chen
We outline the structure of LTM and the systems needed for effective data retention and representation.
1 code implementation • 10 Oct 2024 • Yuanqing Yu, Zhefan Wang, Weizhi Ma, Zhicheng Guo, Jingtao Zhan, Shuai Wang, Chuhan Wu, Zhiqiang Guo, Min Zhang
Despite having powerful reasoning and inference capabilities, Large Language Models (LLMs) still need external tools to acquire real-time information retrieval or domain-specific expertise to solve complex tasks, which is referred to as tool learning.
1 code implementation • 18 Jul 2024 • Yurou Zhao, Yiding Sun, Ruidong Han, Fei Jiang, Lu Guan, Xiang Li, Wei Lin, Weizhi Ma, Jiaxin Mao
However, as current explanation generation methods are commonly trained with an objective to mimic existing user reviews, the generated explanations are often not aligned with the predicted ratings or some important features of the recommended items, and thus, are suboptimal in helping users make informed decision on the recommendation platform.
1 code implementation • 20 Jun 2024 • Yihong Wu, Le Zhang, Fengran Mo, Tianyu Zhu, Weizhi Ma, Jian-Yun Nie
By examining the learning dynamics and equilibrium of the contrastive loss, we offer a fresh lens to understand contrastive learning via graph theory, emphasizing its capability to capture high-order connectivity.
1 code implementation • 5 Jun 2024 • XiaoYu Zhang, Yishan Li, Jiayin Wang, Bowen Sun, Weizhi Ma, Peijie Sun, Min Zhang
We also provide further insights into combining human labels with the LLM evaluation process and utilizing ensembles of multiple heterogeneous LLM evaluators to enhance the accuracy and stability of evaluations.
1 code implementation • 28 May 2024 • Jiayu Li, Hanyu Li, Zhiyu He, Weizhi Ma, Peijie Sun, Min Zhang, Shaoping Ma
However, these libraries often impose certain restrictions on data and seldom support the same model to perform different tasks and input formats, limiting users from customized explorations.
no code implementations • 5 May 2024 • Junkai Li, Yunghwei Lai, Weitao Li, Jingyi Ren, Meng Zhang, Xinhui Kang, Siyu Wang, Peng Li, Ya-Qin Zhang, Weizhi Ma, Yang Liu
The recent rapid development of large language models (LLMs) has sparked a new wave of technological revolution in medical artificial intelligence (AI).
1 code implementation • 24 Apr 2024 • Zhiyu He, Jiayu Li, Weizhi Ma, Min Zhang, Yiqun Liu, Shaoping Ma
Meanwhile, EEG signals are collected with a portable device.
1 code implementation • 22 Apr 2024 • Jiayin Wang, Fengran Mo, Weizhi Ma, Peijie Sun, Min Zhang, Jian-Yun Nie
Based on these ability scores, it is hard for users to determine which LLM best suits their particular needs.
no code implementations • 29 Mar 2024 • Hanyu Li, Weizhi Ma, Peijie Sun, Jiayu Li, Cunxiang Yin, Yancheng He, Guoqiang Xu, Min Zhang, Shaoping Ma
In CUT, user similarity in the target domain is adopted as a constraint for user transformation learning to filter the user collaborative information from the source domain.
1 code implementation • 27 Mar 2024 • Shenghao Yang, Weizhi Ma, Peijie Sun, Qingyao Ai, Yiqun Liu, Mingchen Cai, Min Zhang
Different from previous relation-aware models that rely on predefined rules, we propose to leverage the Large Language Model (LLM) to provide new types of relations and connections between items.
1 code implementation • 27 Mar 2024 • Zhefan Wang, Weizhi Ma, Min Zhang
First, we propose and define the recommendability identification task, which investigates the need for recommendations in the current conversational context.
1 code implementation • 27 Mar 2024 • Shenghao Yang, Weizhi Ma, Peijie Sun, Min Zhang, Qingyao Ai, Yiqun Liu, Mingchen Cai
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance.
1 code implementation • 27 Mar 2024 • Jiayu Li, Peijie Sun, Chumeng Jiang, Weizhi Ma, Qingyao Ai, Min Zhang
In this paper, we provide a new perspective that takes situations as the preconditions for users' interactions.
no code implementations • 25 Feb 2024 • Weitao Li, Junkai Li, Weizhi Ma, Yang Liu
Note that our method is a training-free plug-and-play plugin that is capable of various LLMs.
2 code implementations • 23 Feb 2024 • Zhefan Wang, Yuanqing Yu, Wendi Zheng, Weizhi Ma, Min Zhang
LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks.
1 code implementation • 23 Feb 2024 • Yuanqing Yu, Chongming Gao, Jiawei Chen, Heng Tang, Yuefeng Sun, Qian Chen, Weizhi Ma, Min Zhang
EasyRL4Rec seeks to facilitate the model development and experimental process in the domain of RL-based RSs.
no code implementations • 22 Feb 2024 • Jiayu Li, Aixin Sun, Weizhi Ma, Peijie Sun, Min Zhang
This paper emphasizes the importance of dedicated analyses and methods for domain-specific characteristics for the recommender system studies.
1 code implementation • 5 Feb 2024 • Yifan Wang, Peijie Sun, Weizhi Ma, Min Zhang, Yuan Zhang, Peng Jiang, Shaoping Ma
Fairness of recommender systems (RS) has attracted increasing attention recently.
1 code implementation • 17 Nov 2023 • Shenghao Yang, Chenyang Wang, Yankai Liu, Kangping Xu, Weizhi Ma, Yiqun Liu, Min Zhang, Haitao Zeng, Junlan Feng, Chao Deng
In this paper, we propose CoWPiRec, an approach of Collaborative Word-based Pre-trained item representation for Recommendation.
no code implementations • 19 Jul 2023 • Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, Shen Gao, Jiafeng Guo, Xiangnan He, Yanyan Lan, Chenliang Li, Yiqun Liu, Ziyu Lyu, Weizhi Ma, Jun Ma, Zhaochun Ren, Pengjie Ren, Zhiqiang Wang, Mingwen Wang, Ji-Rong Wen, Le Wu, Xin Xin, Jun Xu, Dawei Yin, Peng Zhang, Fan Zhang, Weinan Zhang, Min Zhang, Xiaofei Zhu
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
1 code implementation • 17 Jul 2023 • Jiayin Wang, Weizhi Ma, Chumeng Jiang, Min Zhang, Yuan Zhang, Biao Li, Peng Jiang
In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items.
2 code implementations • 15 Apr 2023 • Jiayu Li, Peijie Sun, Zhefan Wang, Weizhi Ma, Yangkun Li, Min Zhang, Zhoutian Feng, Daiyue Xue
To address such a task, we propose an Intent-aware ranking Ensemble Learning~(IntEL) model to fuse multiple single-objective item lists with various user intents, in which item-level personalized weights are learned.
no code implementations • 18 Oct 2022 • Yuancheng Sun, Yimeng Chen, Weizhi Ma, Wenhao Huang, Kang Liu, ZhiMing Ma, Wei-Ying Ma, Yanyan Lan
In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively.
2 code implementations • 26 Jun 2022 • Chenyang Wang, Yuanqing Yu, Weizhi Ma, Min Zhang, Chong Chen, Yiqun Liu, Shaoping Ma
Then, we empirically analyze the learning dynamics of typical CF methods in terms of quantified alignment and uniformity, which shows that better alignment or uniformity both contribute to higher recommendation performance.
no code implementations • 8 Jun 2022 • Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, Shaoping Ma
First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues.
no code implementations • 5 Apr 2022 • Yangkun Li, Weizhi Ma, Chong Chen, Min Zhang, Yiqun Liu, Shaoping Ma, Yuekui Yang
Among various methods of coping with overfitting, dropout is one of the representative ways.
2 code implementations • 11 Jun 2021 • Bin Hao, Min Zhang, Weizhi Ma, Shaoyun Shi, Xinxing Yu, Houzhi Shan, Yiqun Liu, Shaoping Ma
To the best of our knowledge, this is the largest real-world interaction dataset for personalized recommendation.
3 code implementations • 20 Aug 2020 • Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, Yongfeng Zhang
Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs.
2 code implementations • 1 Jul 2020 • Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma
However, existing KG enhanced recommendation methods have largely focused on exploring advanced neural network architectures to better investigate the structural information of KG.
2 code implementations • WWW 2020 • Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, and Shaoping Ma Department of Computer Science and Technology, Institute for Articial Intelligence, Beijing National Research Center for Information Science and Technology, Tsinghua University cc17@mails.tsinghua.edu.cn, z-m@tsinghua.edu.cn
Factorization Machines (FM) with negative sampling is a popular solution for context-aware recommendation.
1 code implementation • 9 Mar 2019 • Weizhi Ma, Min Zhang, Yue Cao, Woojeong, Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, Xiang Ren
The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue.