29 citations · 73 across the 31 of their papers we have counts for
33 papers
AMBER: Multi-View Adaptive Budget Allocation for Listwise Vision-Language Reranking
Wenteng Chen, Jiachen Zhu, Rong Shan +10
Vision-language models (VLMs) are powerful listwise rerankers for multimodal retrieval, but high inference costs restrict them to evaluating small local candidate views. Existing m…
Weaving Visual Narratives: Agentic Image Bundle Composition Beyond Atomic Visual Matching
Rong Shan, Tianyi Xu, Congmin Zheng +9
Image retrieval has traditionally been formulated as a point-wise matching problem, where each candidate image is scored in isolation. However, this atomic paradigm fails to captur…
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…
MuonRec: Shifting the Optimizer Paradigm Beyond Adam in Scalable Generative Recommendation
Rong Shan, Aofan Yu, Bo Chen +7
Recommender systems (RecSys) are increasingly emphasizing scaling, leveraging larger architectures and more interaction data to improve personalization. Yet, despite the optimizer'…
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…
Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey
Jiachen Zhu, Menghui Zhu, Renting Rui +9
The advent of large language models (LLMs), such as GPT, Gemini, and DeepSeek, has significantly advanced natural language processing, giving rise to sophisticated chatbots capable…
Superplatforms Have to Attack AI Agents
Jianghao Lin, Jiachen Zhu, Zheli Zhou +4
Over the past decades, superplatforms, digital companies that integrate a vast range of third-party services and applications into a single, unified ecosystem, have built their for…
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…
Position: The Real Barrier to LLM Agent Usability is Agentic ROI
Weiwen Liu, Jiarui Qin, Xu Huang +10
Large Language Model (LLM) agents represent a promising shift in human-AI interaction, moving beyond passive prompt-response systems to autonomous agents capable of reasoning, plan…
NL-Debugging: Exploiting Natural Language as an Intermediate Representation for Code Debugging
Weiming Zhang, Qingyao Li, Xinyi Dai +7
Debugging is a critical aspect of LLM's coding ability. Early debugging efforts primarily focused on code-level analysis, which often falls short when addressing complex programmin…
A Survey of AI Agent Protocols
Yingxuan Yang, Huacan Chai, Yuanyi Song +11
The rapid development of large language models (LLMs) has led to the widespread deployment of LLM agents across diverse industries, including customer service, content generation,…
RecCocktail: A Generalizable and Efficient Framework for LLM-Based Recommendation
Min Hou, Chenxi Bai, Le Wu +6
Large Language Models (LLMs) have achieved remarkable success in recent years, owing to their impressive generalization capabilities and rich world knowledge. To capitalize on the…
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…
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…
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…
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…
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…
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…
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…
Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models
Yunjia Xi, Weiwen Liu, Jianghao Lin +5
The rise of large language models (LLMs) has opened new opportunities in Recommender Systems (RSs) by enhancing user behavior modeling and content understanding. However, current a…
D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems
Jiarui Qin, Weiwen Liu, Ruiming Tang +2
A vast amount of user behavior data is constantly accumulating on today's large recommendation platforms, recording users' various interests and tastes. Preserving knowledge from t…
Ten Challenges in Industrial Recommender Systems
Zhenhua Dong, Jieming Zhu, Weiwen Liu +1
Huawei's vision and mission is to build a fully connected intelligent world. Since 2013, Huawei Noah's Ark Lab has helped many products build recommender systems and search engines…
Multi-domain Recommendation with Embedding Disentangling and Domain Alignment
Wentao Ning, Xiao Yan, Weiwen Liu +3
Multi-domain recommendation (MDR) aims to provide recommendations for different domains (e.g., types of products) with overlapping users/items and is common for platforms such as A…
Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation
Xianyu Chen, Jian Shen, Wei Xia +9
With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation s…