98 citations · 486 across the 56 of their papers we have counts for
26 papers · 1 filter
ChatShopBuddy: Towards Reliable Conversational Shopping Agents via Reinforcement Learning
Yiruo Cheng, Kelong Mao, Tianhao Li +3
Conversational shopping agents represent a critical consumer-facing application of Large Language Model (LLM)-powered agents, yet how to effectively apply post-training Reinforceme…
MemSifter: Offloading LLM Memory Retrieval via Outcome-Driven Proxy Reasoning
Jiejun Tan, Zhicheng Dou, Liancheng Zhang +3
As Large Language Models (LLMs) are increasingly used for long-duration tasks, maintaining effective long-term memory has become a critical challenge. Current methods often face a…
HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches
Jiejun Tan, Zhicheng Dou, Yan Yu +4
Recently, large reasoning models have demonstrated strong mathematical and coding abilities, and deep search leverages their reasoning capabilities in challenging information retri…
FairDiverse: A Comprehensive Toolkit for Fair and Diverse Information Retrieval Algorithms
Chen Xu, Zhirui Deng, Clara Rus +6
In modern information retrieval (IR). achieving more than just accuracy is essential to sustaining a healthy ecosystem, especially when addressing fairness and diversity considerat…
HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems
Jiejun Tan, Zhicheng Dou, Wen Wang +3
Retrieval-Augmented Generation (RAG) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs. The Web is a major source of external knowled…
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
Qingyao Ai, Ting Bai, Zhao Cao +30
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Mod…
RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
Jiongnan Liu, Jiajie Jin, Zihan Wang +3
Although Large Language Models (LLMs) have demonstrated extraordinary capabilities in many domains, they still have a tendency to hallucinate and generate fictitious responses to u…
User Behavior Simulation with Large Language Model based Agents
Lei Wang, Jingsen Zhang, Hao Yang +11
Simulating high quality user behavior data has always been a fundamental problem in human-centered applications, where the major difficulty originates from the intricate mechanism…
JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions
Jiongnan Liu, Zhicheng Dou, Guoyu Tang +1
Recently, personalized product search attracts great attention and many models have been proposed. To evaluate the effectiveness of these models, previous studies mainly utilize th…
Generative Retrieval via Term Set Generation
Peitian Zhang, Zheng Liu, Yujia Zhou +3
Recently, generative retrieval emerges as a promising alternative to traditional retrieval paradigms. It assigns each document a unique identifier, known as DocID, and employs a ge…
Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search
Kelong Mao, Zhicheng Dou, Fengran Mo +3
Precisely understanding users' contextual search intent has been an important challenge for conversational search. As conversational search sessions are much more diverse and long-…
CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection
Jing Yao, Zheng Liu, Junhan Yang +3
Deep semantic matching aims to discriminate the relationship between documents based on deep neural networks. In recent years, it becomes increasingly popular to organize documents…
From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking
Yutao Zhu, Jian-Yun Nie, Yixuan Su +3
Contextual information in search sessions is important for capturing users' search intents. Various approaches have been proposed to model user behavior sequences to improve docume…
Pre-training for Information Retrieval: Are Hyperlinks Fully Explored?
Jiawen Wu, Xinyu Zhang, Yutao Zhu +7
Recent years have witnessed great progress on applying pre-trained language models, e.g., BERT, to information retrieval (IR) tasks. Hyperlinks, which are commonly used in Web page…
Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search
Haonan Chen, Zhicheng Dou, Yutao Zhu +3
Users' search tasks have become increasingly complicated, requiring multiple queries and interactions with the results. Recent studies have demonstrated that modeling the historica…
Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer
Yujia Zhou, Jing Yao, Zhicheng Dou +3
Document retrieval has been extensively studied within the index-retrieve framework for decades, which has withstood the test of time. Unfortunately, such a pipelined framework lim…
DynamicRetriever: A Pre-training Model-based IR System with Neither Sparse nor Dense Index
Yujia Zhou, Jing Yao, Zhicheng Dou +2
Web search provides a promising way for people to obtain information and has been extensively studied. With the surgence of deep learning and large-scale pre-training techniques, v…
Socialformer: Social Network Inspired Long Document Modeling for Document Ranking
Yujia Zhou, Zhicheng Dou, Huaying Yuan +1
Utilizing pre-trained language models has achieved great success for neural document ranking. Limited by the computational and memory requirements, long document modeling becomes a…
Group based Personalized Search by Integrating Search Behaviour and Friend Network
Yujia Zhou, Zhicheng Dou, Bingzheng Wei +1
The key to personalized search is to build the user profile based on historical behaviour. To deal with the users who lack historical data, group based personalized models were pro…
PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling
Yujia Zhou, Zhicheng Dou, Yutao Zhu +1
Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors. Previous studies have made g…
Learning to Select Historical News Articles for Interaction based Neural News Recommendation
Peitian Zhang, Zhicheng Dou, Jing Yao
The key to personalized news recommendation is to match the user's interests with the candidate news precisely and efficiently. Most existing approaches embed user interests into a…
USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence
Jing Yao, Zhicheng Dou, Ruobing Xie +3
Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right t…
Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking
Yutao Zhu, Jian-Yun Nie, Zhicheng Dou +5
Context information in search sessions has proven to be useful for capturing user search intent. Existing studies explored user behavior sequences in sessions in different ways to…
Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need
Zhengyi Ma, Zhicheng Dou, Wei Xu +4
Designing pre-training objectives that more closely resemble the downstream tasks for pre-trained language models can lead to better performance at the fine-tuning stage, especiall…