25 citations · 97 across the 31 of their papers we have counts for
19 papers · 1 filter
The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation
Ziwei Liu, Yejing Wang, Wanyu Wang +6
Conventional Sequential Recommender Systems (SRS) typically assign unique hash IDs (HID) to construct item embeddings, which mainly capture collaborative signals from historical us…
LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training
Ziwei Liu, Qidong Liu, Wanyu Wang +6
Cross-domain Sequential Recommendation (CDSR) has been proposed to enrich user-item interactions by incorporating information from various domains. Despite current progress, the im…
GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks
Yejing Wang, Shengyu Zhou, Jinyu Lu +9
Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scen…
Bridge the Domains: Large Language Models Enhanced Cross-domain Sequential Recommendation
Qidong Liu, Xiangyu Zhao, Yejing Wang +6
Cross-domain Sequential Recommendation (CDSR) aims to extract the preference from the user's historical interactions across various domains. Despite some progress in CDSR, two prob…
LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation
Yuqi Sun, Qidong Liu, Haiping Zhu +1
Sequential Recommender Systems (SRS) have become a cornerstone of online platforms, leveraging users' historical interaction data to forecast their next potential engagement. Despi…
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…
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…
A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication Recommendation
Qidong Liu, Zhaopeng Qiu, Xiangyu Zhao +4
Medication recommendation is one of the most critical health-related applications, which has attracted extensive research interest recently. Most existing works focus on a single h…
LLM-Powered User Simulator for Recommender System
Zijian Zhang, Shuchang Liu, Ziru Liu +6
User simulators can rapidly generate a large volume of timely user behavior data, providing a testing platform for reinforcement learning-based recommender systems, thus accelerati…
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…
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…
GPRec: Bi-level User Modeling for Deep Recommenders
Yejing Wang, Dong Xu, Xiangyu Zhao +7
GPRec explicitly categorizes users into groups in a learnable manner and aligns them with corresponding group embeddings. We design the dual group embedding space to offer a divers…
LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation
Qidong Liu, Xian Wu, Wanyu Wang +5
Sequential Recommender Systems (SRS), which model a user's interaction history to predict the next item of interest, are widely used in various applications. However, existing SRS…
M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework
Zijian Zhang, Shuchang Liu, Jiaao Yu +9
Multi-domain recommendation and multi-task recommendation have demonstrated their effectiveness in leveraging common information from different domains and objectives for comprehen…
LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation
Qidong Liu, Xian Wu, Yejing Wang +4
Sequential recommender systems (SRS) aim to predict users' subsequent choices based on their historical interactions and have found applications in diverse fields such as e-commerc…
Large Language Model Distilling Medication Recommendation Model
Qidong Liu, Xian Wu, Xiangyu Zhao +4
The recommendation of medication is a vital aspect of intelligent healthcare systems, as it involves prescribing the most suitable drugs based on a patient's specific health needs.…
Diffusion Augmentation for Sequential Recommendation
Qidong Liu, Fan Yan, Xiangyu Zhao +4
Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions…
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…