9 citations · 26 across the 51 of their papers we have counts for
22 papers · 1 filter
UNIQUE: A Unified Retrieval and Ranking System for Large-Scale Feed Recommendation
Zhuang Liu, Yongkang Fu, Zuodong Yang +6
Industrial mobile feed systems rely on a retrieval-ranking pipeline to serve large-scale, heterogeneous, and fast-changing content under strict latency constraints. However, existi…
MuSeR: Scalable Long-sequence Recommendation with Multi-interest Modeling
Yongkang Fu, Beining Bao, Yu Jiang +10
Ultra-long user behavior sequences carry rich signals of stable and diverse preferences, yet industrial recommender systems typically truncate histories to a few hundred actions un…
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…
Reinforced Preference Optimization for Reasoning-Augmented Recommendations
Jingtong Gao, Zeyu Song, Chi Lu +7
Recommender systems are critical for delivering personalized content across digital platforms, and recent advances in Large Language Models (LLMs) offer new opportunities to enhanc…
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…
Rethinking Semantic Collaborative Integration: Why Alignment Is Not Enough
Maolin Wang, Dongze Wu, Jianing Zhou +7
Large language models (LLMs) have become an important semantic infrastructure for modern recommender systems. A prevailing paradigm integrates LLM-derived semantic embeddings with…
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…
Empowering Denoising Sequential Recommendation with Large Language Model Embeddings
Tongzhou Wu, Yuhao Wang, Maolin Wang +2
Sequential recommendation aims to capture user preferences by modeling sequential patterns in user-item interactions. However, these models are often influenced by noise such as ac…
SPARK: Adaptive Low-Rank Knowledge Graph Modeling in Hybrid Geometric Spaces for Recommendation
Binhao Wang, Yutian Xiao, Maolin Wang +4
Knowledge Graphs (KGs) enhance recommender systems but face challenges from inherent noise, sparsity, and Euclidean geometry's inadequacy for complex relational structures, critica…
Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs
Yuhao Wang, Junwei Pan, Xinhang Li +6
Sequential recommendation (SR) aims to capture users' dynamic interests and sequential patterns based on their historical interactions. Recently, the powerful capabilities of large…
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation
Maolin Wang, Yutian Xiao, Binhao Wang +6
Modern recommendation systems face significant challenges in processing multimodal sequential data, particularly in temporal dynamics modeling and information flow coordination. Tr…
Measure Domain's Gap: A Similar Domain Selection Principle for Multi-Domain Recommendation
Yi Wen, Yue Liu, Derong Xu +9
Multi-Domain Recommendation (MDR) achieves the desirable recommendation performance by effectively utilizing the transfer information across different domains. Despite the great su…
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation
Maolin Wang, Sheng Zhang, Ruocheng Guo +6
Recent deep sequential recommendation models often struggle to effectively model key characteristics of user behaviors, particularly in handling sequence length variations and capt…
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…
Behavior Modeling Space Reconstruction for E-Commerce Search
Yejing Wang, Chi Zhang, Xiangyu Zhao +8
Delivering superior search services is crucial for enhancing customer experience and driving revenue growth. Conventionally, search systems model user behaviors by combining user p…
Large Language Model Enhanced Recommender Systems: A Survey
Qidong Liu, Xiangyu Zhao, Yuhao Wang +9
Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the…
Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models
Yuhao Wang, Junwei Pan, Pengyue Jia +6
Sequential Recommendation (SR) aims to leverage the sequential patterns in users' historical interactions to accurately track their preferences. However, the primary reliance of ex…
ScalingNote: Scaling up Retrievers with Large Language Models for Real-World Dense Retrieval
Suyuan Huang, Chao Zhang, Yuanyuan Wu +12
Dense retrieval in most industries employs dual-tower architectures to retrieve query-relevant documents. Due to online deployment requirements, existing real-world dense retrieval…
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems
Sheng Zhang, Maolin Wang, Wanyu Wang +6
Transformer-based models have gained significant traction in sequential recommender systems (SRSs) for their ability to capture user-item interactions effectively. However, these m…
DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems
Sheng Zhang, Maolin Wang, Yao Zhao +6
In the era of data proliferation, efficiently sifting through vast information to extract meaningful insights has become increasingly crucial. This paper addresses the computationa…
Embedding in Recommender Systems: A Survey
Maolin Wang, Xinjian Zhao, Wanyu Wang +9
Recommender systems have become an essential component of many online platforms, providing personalized recommendations to users. A crucial aspect is embedding techniques that conv…