5 citations · 19 across the 29 of their papers we have counts for
27 papers · 1 filter
R-Searcher: Calibrating Retrieval and Reasoning Boundaries for Agentic Search
Sheng Zhang, Junyi Li, Wenlin Zhang +6
Recent search agents for multi-hop reasoning often fail by either retrieving incomplete evidence or reasoning over irrelevant portions of the retrieved content, leading to a retrie…
RAGR: Review-Augmented Generative Recommendation
Yingyi Zhang, Junyi Li, Yejing Wang +8
Sequential recommendation (SR) is traditionally formulated as next-item prediction over chronological item interactions. Although recent generative recommendation (GR) methods intr…
MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search
Sheng Zhang, Junyi Li, Yingyi Zhang +7
Recent advances in large language models (LLMs) have scaled the potential for reasoning and agentic search, wherein models autonomously plan, retrieve, and reason over external kno…
Personalized Deep Research: A User-Centric Framework, Dataset, and Hybrid Evaluation for Knowledge Discovery
Xiaopeng Li, Wenlin Zhang, Yingyi Zhang +6
Deep Research agents driven by LLMs have automated the scholarly discovery pipeline, from planning and query formulation to iterative web exploration. Yet they remain constrained b…
Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval
Yingyi Zhang, Junyi Li, Wenlin Zhang +8
Personalized large language models (LLMs) rely on memory retrieval to incorporate user-specific histories, preferences, and contexts. Existing approaches either overload the LLM by…
To Search or Not to Search: Aligning the Decision Boundary of Deep Search Agents via Causal Intervention
Wenlin Zhang, Kuicai Dong, Junyi Li +9
Deep search agents, which autonomously iterate through multi-turn web-based reasoning, represent a promising paradigm for complex information-seeking tasks. However, current agents…
Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testing
Wenlin Zhang, Xiangyang Li, Qiyuan Ge +9
In recommender systems, online A/B testing is a crucial method for evaluating the performance of different models. However, conducting online A/B testing often presents significant…
Doc-Researcher: A Unified System for Multimodal Document Parsing and Deep Research
Kuicai Dong, Shurui Huang, Fangda Ye +9
Deep Research systems have revolutionized how LLMs solve complex questions through iterative reasoning and evidence gathering. However, current systems remain fundamentally constra…
Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval
Yingyi Zhang, Pengyue Jia, Derong Xu +9
Retrieval-Augmented Generation (RAG) critically depends on effective query expansion to retrieve relevant information. However, existing expansion methods adopt uniform strategies…
Deep Research: A Survey of Autonomous Research Agents
Wenlin Zhang, Xiaopeng Li, Yingyi Zhang +5
The rapid advancement of large language models (LLMs) has driven the development of agentic systems capable of autonomously performing complex tasks. Despite their impressive capab…
Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
Wenlin Zhang, Xiangyang Li, Kuicai Dong +9
Retrieval-augmented generation (RAG) enhances the text generation capabilities of large language models (LLMs) by integrating external knowledge and up-to-date information. However…
LSRP: A Leader-Subordinate Retrieval Framework for Privacy-Preserving Cloud-Device Collaboration
Yingyi Zhang, Pengyue Jia, Xianneng Li +8
Cloud-device collaboration leverages on-cloud Large Language Models (LLMs) for handling public user queries and on-device Small Language Models (SLMs) for processing private user d…
From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs
Yaxiong Wu, Sheng Liang, Chen Zhang +5
Memory is the process of encoding, storing, and retrieving information, allowing humans to retain experiences, knowledge, skills, and facts over time, and serving as the foundation…
A Survey of Personalization: From RAG to Agent
Xiaopeng Li, Pengyue Jia, Derong Xu +11
Personalization has become an essential capability in modern AI systems, enabling customized interactions that align with individual user preferences, contexts, and goals. Recent r…
Joint Modeling in Recommendations: A Survey
Xiangyu Zhao, Yichao Wang, Bo Chen +7
In today's digital landscape, Deep Recommender Systems (DRS) play a crucial role in navigating and customizing online content for individual preferences. However, conventional meth…
SampleLLM: Optimizing Tabular Data Synthesis in Recommendations
Jingtong Gao, Zhaocheng Du, Xiaopeng Li +5
Tabular data synthesis is crucial in machine learning, yet existing general methods-primarily based on statistical or deep learning models-are highly data-dependent and often fall…
SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval
Xiaopeng Li, Xiangyang Li, Hao Zhang +6
The performance of Dense retrieval (DR) is significantly influenced by the quality of negative sampling. Traditional DR methods primarily depend on naive negative sampling techniqu…
Scenario-Wise Rec: A Multi-Scenario Recommendation Benchmark
Xiaopeng Li, Jingtong Gao, Pengyue Jia +7
Multi Scenario Recommendation (MSR) tasks, referring to building a unified model to enhance performance across all recommendation scenarios, have recently gained much attention. Ho…
SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems
Pengyue Jia, Zhaocheng Du, Yichao Wang +6
Feature selection is crucial in recommender systems for improving model efficiency and predictive performance. Conventional approaches typically employ surrogate models-such as dec…
Prompt Tuning as User Inherent Profile Inference Machine
Yusheng Lu, Zhaocheng Du, Xiangyang Li +9
Large Language Models (LLMs) have exhibited significant promise in recommender systems by empowering user profiles with their extensive world knowledge and superior reasoning capab…
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
Bo Chen, Xinyi Dai, Huifeng Guo +9
Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs. The emergence of large lan…
LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation
Yuhao Wang, Yichao Wang, Zichuan Fu +4
As the demand for more personalized recommendation grows and a dramatic boom in commercial scenarios arises, the study on multi-scenario recommendation (MSR) has attracted much att…
LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations
Jingtong Gao, Bo Chen, Weiwen Liu +6
Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms. Traditional reranking models have focused p…
M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
Jiachen Zhu, Yichao Wang, Jianghao Lin +4
We primarily focus on the field of multi-scenario recommendation, which poses a significant challenge in effectively leveraging data from different scenarios to enhance predictions…