31 citations · 74 across the 63 of their papers we have counts for
37 papers · 1 filter
Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation
Kangning Zhang, Haotian Fang, Xukun Luo +6
Semantic-ID generative recommenders represent each item as a short sequence of discrete semantic tokens and predict the next item by autoregressively generating this token sequence…
DiffCold: A Diffusion-based Generative Model for Cold-Start Item Recommendation
Kangning Zhang, Yingjie Qin, Weinan Zhang +2
Cold-start item recommendation remains a persistent challenge in real-world systems due to the absence of interaction histories. While prior models attempt to bridge this gap using…
DynaTree: Dynamic Agentic Retrieval Tree for Time-Sensitive News Retrieval
Siyuan Qi, Xinyuan Wang, Yingxuan Yang +5
Agentic Retrieval-Augmented Generation improves retrieval by integrating planning, tool use, and iterative reasoning, but existing agentic RAG methods often couple semantic expansi…
Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations
Yunjia Xi, Menghui Zhu, Jianghao Lin +4
Recently, large language models (LLMs) have advanced recommendation systems (RSs), and recent works have begun to explore how to integrate LLMs into industrial RSs. While most appr…
A Survey of LLM-based Deep Search Agents: Paradigm, Optimization, Evaluation, and Challenges
Yunjia Xi, Jianghao Lin, Yongzhao Xiao +7
The advent of Large Language Models (LLMs) has significantly revolutionized web search. The emergence of LLM-based Search Agents marks a pivotal shift towards deeper, dynamic, auto…
MassTool: A Multi-Task Search-Based Tool Retrieval Framework for Large Language Models
Jianghao Lin, Xinyuan Wang, Xinyi Dai +5
Tool retrieval is a critical component in enabling large language models (LLMs) to interact effectively with external tools. It aims to precisely filter the massive tools into a sm…
Generative Representational Learning of Foundation Models for Recommendation
Zheli Zhou, Chenxu Zhu, Jianghao Lin +4
Developing a single foundation model with the capability to excel across diverse tasks has been a long-standing objective in the field of artificial intelligence. As the wave of ge…
InfoDeepSeek: Benchmarking Agentic Information Seeking for Retrieval-Augmented Generation
Yunjia Xi, Jianghao Lin, Menghui Zhu +10
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by grounding responses with retrieved information. As an emerging paradigm, Agentic RAG further enhances…
Sell It Before You Make It: Revolutionizing E-Commerce with Personalized AI-Generated Items
Jianghao Lin, Peng Du, Jiaqi Liu +4
E-commerce has revolutionized retail, yet its traditional workflows remain inefficient, with significant resource costs tied to product design and inventory. This paper introduces…
Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
Yunjia Xi, Muyan Weng, Wen Chen +9
Recommender systems (RSs) often suffer from the feedback loop phenomenon, e.g., RSs are trained on data biased by their recommendations. This leads to the filter bubble effect that…
Full-Stack Optimized Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
Rong Shan, Jiachen Zhu, Jianghao Lin +5
In this paper, we address the lifelong sequential behavior incomprehension problem in large language models (LLMs) for recommendation, where LLMs struggle to extract useful informa…
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
Junjie Huang, Jiarui Qin, Yong Yu +1
Given the large volume of side information from different modalities, multimodal recommender systems have become increasingly vital, as they exploit richer semantic information bey…
Why Not Together? A Multiple-Round Recommender System for Queries and Items
Jiarui Jin, Xianyu Chen, Weinan Zhang +2
A fundamental technique of recommender systems involves modeling user preferences, where queries and items are widely used as symbolic representations of user interests. Queries de…
An Automatic Graph Construction Framework based on Large Language Models for Recommendation
Rong Shan, Jianghao Lin, Chenxu Zhu +7
Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation method…
LIBER: Lifelong User Behavior Modeling Based on Large Language Models
Chenxu Zhu, Shigang Quan, Bo Chen +7
CTR prediction plays a vital role in recommender systems. Recently, large language models (LLMs) have been applied in recommender systems due to their emergence abilities. While le…
Beyond Positive History: Re-ranking with List-level Hybrid Feedback
Muyan Weng, Yunjia Xi, Weiwen Liu +5
As the last stage of recommender systems, re-ranking generates a re-ordered list that aligns with the user's preference. However, previous works generally focus on item-level posit…
Unleashing the Potential of Multi-Channel Fusion in Retrieval for Personalized Recommendations
Junjie Huang, Jiarui Qin, Jianghao Lin +3
Recommender systems (RS) are pivotal in managing information overload in modern digital services. A key challenge in RS is efficiently processing vast item pools to deliver highly…
MOTOR: Learning ID-free Item Representation with Token Crossing for Embedding-based Multimodal Recommendation
Kangning Zhang, Jiarui Jin, Yingjie Qin +4
While multimodal recommendation models have effectively integrated visual and textual information, their reliance on unique ID embeddings constitutes a fundamental performance bott…
A Survey on Diffusion Models for Recommender Systems
Jianghao Lin, Jiaqi Liu, Jiachen Zhu +5
While traditional recommendation techniques have made significant strides in the past decades, they still suffer from limited generalization performance caused by factors like inad…
Efficient and Deployable Knowledge Infusion for Open-World Recommendations via Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin +8
Recommender systems (RSs) play a pervasive role in today's online services, yet their closed-loop nature constrains their access to open-world knowledge. Recently, large language m…
Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation
Jiachen Zhu, Jianghao Lin, Xinyi Dai +6
We primarily focus on the field of large language models (LLMs) for recommendation, which has been actively explored recently and poses a significant challenge in effectively enhan…
Efficiency Unleashed: Inference Acceleration for LLM-based Recommender Systems with Speculative Decoding
Yunjia Xi, Hangyu Wang, Bo Chen +7
The past few years have witnessed a growing interest in LLM-based recommender systems (RSs), although their industrial deployment remains in a preliminary stage. Most existing depl…
MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin +4
Conversational recommender systems (CRSs) aim to capture user preferences and provide personalized recommendations through multi-round natural language dialogues. However, most exi…
A Comprehensive Survey on Retrieval Methods in Recommender Systems
Junjie Huang, Jizheng Chen, Jianghao Lin +4
In an era dominated by information overload, effective recommender systems are essential for managing the deluge of data across digital platforms. Multi-stage cascade ranking syste…