Search Results for author: Weizhi Ma

Found 36 papers, 26 papers with code

Interpret the Internal States of Recommendation Model with Sparse Autoencoder

1 code implementation9 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.

Explainable Recommendation Fairness +1

Beyond Utility: Evaluating LLM as Recommender

1 code implementation1 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.

Position Re-Ranking

PerSRV: Personalized Sticker Retrieval with Vision-Language Model

1 code implementation29 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.

Language Modeling Language Modelling +2

R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation

no code implementations27 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.

Information Retrieval Language Modelling +3

Long Term Memory: The Foundation of AI Self-Evolution

no code implementations21 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.

StepTool: A Step-grained Reinforcement Learning Framework for Tool Learning in LLMs

1 code implementation10 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.

Information Retrieval Policy Gradient Methods

Aligning Explanations for Recommendation with Rating and Feature via Maximizing Mutual Information

1 code implementation18 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.

Explanation Generation

Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering

1 code implementation20 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.

Collaborative Filtering Contrastive Learning

Large Language Models as Evaluators for Recommendation Explanations

1 code implementation5 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.

Common Sense Reasoning Instruction Following +3

ReChorus2.0: A Modular and Task-Flexible Recommendation Library

1 code implementation28 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.

Click-Through Rate Prediction Recommendation Systems

Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents

no code implementations5 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).

MedQA Question Answering

A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models

1 code implementation22 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.

Benchmarking World Knowledge

Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation

no code implementations29 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.

Sequential Recommendation with Latent Relations based on Large Language Model

1 code implementation27 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.

Collaborative Filtering Knowledge Graphs +5

To Recommend or Not: Recommendability Identification in Conversations with Pre-trained Language Models

1 code implementation27 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.

Recommendation Systems

Common Sense Enhanced Knowledge-based Recommendation with Large Language Model

1 code implementation27 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.

Common Sense Reasoning Knowledge Graphs +3

A Situation-aware Enhancer for Personalized Recommendation

1 code implementation27 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.

Recommendation Systems

Citation-Enhanced Generation for LLM-based Chatbots

no code implementations25 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.

Chatbot Citation Prediction +3

MACRec: a Multi-Agent Collaboration Framework for Recommendation

2 code implementations23 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.

Conversational Recommendation Decision Making +2

Recommender for Its Purpose: Repeat and Exploration in Food Delivery Recommendations

no code implementations22 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.

Recommendation Systems

Measuring Item Global Residual Value for Fair Recommendation

1 code implementation17 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.

Recommendation Systems

Intent-aware Ranking Ensemble for Personalized Recommendation

2 code implementations15 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.

Ensemble Learning Recommendation Systems

PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction

no code implementations18 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.

Drug Discovery Molecular Property Prediction +3

Towards Representation Alignment and Uniformity in Collaborative Filtering

2 code implementations26 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.

Collaborative Filtering Graph Neural Network

A Survey on the Fairness of Recommender Systems

no code implementations8 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.

Fairness Recommendation Systems +1

Neural Logic Reasoning

3 code implementations20 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.

Logical Reasoning Recommendation Systems

Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation

2 code implementations1 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.

Knowledge Graph Embedding Knowledge Graphs +2

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

1 code implementation9 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.

Explainable Recommendation Knowledge Graphs +1

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