86 citations · 186 across the 22 of their papers we have counts for
22 papers
Hierarchical Quantization with Domain-Adaptive Sparse Routing for Generative Cross-Domain Recommendation
Haiying He, Xiaopeng Li, Yuchen Gu +9
Generative Recommendation (GenRec) represents a promising paradigm that achieves remarkable empirical success by encoding items as compact Semantic IDs (SIDs) and modeling user beh…
Hierarchical Residual Policy Optimization for Generative Recommendations
Kaifeng Guo, Yiming Yang, Jingtong Gao +6
Generative recommenders select items by autoregressively decoding semantic identifiers (SIDs), whose token positions induce a coarse-to-fine hierarchy over the item space. In pract…
Escaping the Euclidean Void: Manifold-Informed Flow Matching for Sequential Recommendation
Dengzhao Fang, Jingtong Gao, Yu Li +2
Conventional recommenders capture users' preferences by optimizing observed user-item relations, whereas continuous generative recommendation additionally learns the trajectory of…
Detecting Miscitation on the Scholarly Web through LLM-Augmented Text-Rich Graph Learning
Huidong Wu, Haojia Xiang, Jingtong Gao +3
Scholarly web is a vast network of knowledge connected by citations. However, this system is increasingly compromised by miscitation, where references do not support or even contra…
Enhancing Conversational Agents via Task-Oriented Adversarial Memory Adaptation
Yimin Deng, Yuqing Fu, Derong Xu +10
Conversational agents struggle to handle long conversations due to context window limitations. Therefore, memory systems are developed to leverage essential historical information.…
PRISM: Purified Representation and Integrated Semantic Modeling for Generative Sequential Recommendation
Dengzhao Fang, Jingtong Gao, Yu Li +2
Generative Sequential Recommendation (GSR) has emerged as a promising paradigm, reframing recommendation as an autoregressive sequence generation task over discrete Semantic IDs (S…
BlossomRec: Block-level Fused Sparse Attention Mechanism for Sequential Recommendations
Mengyang Ma, Xiaopeng Li, Wanyu Wang +9
Transformer structures have been widely used in sequential recommender systems (SRS). However, as user interaction histories increase, computational time and memory requirements al…
HiD-VAE: Interpretable Generative Recommendation via Hierarchical and Disentangled Semantic IDs
Dengzhao Fang, Jingtong Gao, Chengcheng Zhu +3
Recommender systems are indispensable for helping users navigate the immense item catalogs of modern online platforms. Recently, generative recommendation has emerged as a promisin…
Graph Federated Learning for Personalized Privacy Recommendation
Ce Na, Kai Yang, Dengzhao Fang +6
Federated recommendation systems (FedRecs) have gained significant attention for providing privacy-preserving recommendation services. However, existing FedRecs assume that all use…
Navigate the Unknown: Enhancing LLM Reasoning with Intrinsic Motivation Guided Exploration
Jingtong Gao, Ling Pan, Yejing Wang +6
Reinforcement Learning (RL) has become a key approach for enhancing the reasoning capabilities of large language models. However, prevalent RL approaches like proximal policy optim…
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…
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…
LinRec: Linear Attention Mechanism for Long-term Sequential Recommender Systems
Langming Liu, Xiangyu Zhao, Chi Zhang +7
Transformer models have achieved remarkable success in sequential recommender systems (SRSs). However, computing the attention matrix in traditional dot-product attention mechanism…
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…
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…
BiVRec: Bidirectional View-based Multimodal Sequential Recommendation
Jiaxi Hu, Jingtong Gao, Xiangyu Zhao +6
The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research. In the initial stages of multimodal sequential…
Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation
Jingtong Gao, Bo Chen, Menghui Zhu +6
Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
Multi-Task Recommendations with Reinforcement Learning
Ziru Liu, Jiejie Tian, Qingpeng Cai +8
In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard t…
Multimodal Recommender Systems: A Survey
Qidong Liu, Jiaxi Hu, Yutian Xiao +5
The recommender system (RS) has been an integral toolkit of online services. They are equipped with various deep learning techniques to model user preference based on identifier an…
A Comprehensive Survey on Trustworthy Recommender Systems
Wenqi Fan, Xiangyu Zhao, Xiao Chen +8
As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalize…