359 citations · 681 across the 38 of their papers we have counts for
26 papers · 1 filter
Human vs. Agent in Task-Oriented Conversations
Zhefan Wang, Ning Geng, Zhiqiang Guo +2
Task-oriented conversational systems are essential for efficiently addressing diverse user needs, yet their development requires substantial amounts of high-quality conversational…
Are Recommenders Self-Aware? Label-Free Recommendation Performance Estimation via Model Uncertainty
Jiayu Li, Ziyi Ye, Guohao Jian +4
Can a recommendation model be self-aware? This paper investigates the recommender's self-awareness by quantifying its uncertainty, which provides a label-free estimation of its per…
Short Video Segment-level User Dynamic Interests Modeling in Personalized Recommendation
Zhiyu He, Zhixin Ling, Jiayu Li +5
The rapid growth of short videos has necessitated effective recommender systems to match users with content tailored to their evolving preferences. Current video recommendation mod…
U-Sticker: A Large-Scale Multi-Domain User Sticker Dataset for Retrieval and Personalization
Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo +3
Instant messaging with texts and stickers has become a widely adopted communication medium, enabling efficient expression of user semantics and emotions. With the increased use of…
Understanding Internal Representations of Recommendation Models with Sparse Autoencoders
Jiayin Wang, Xiaoyu Zhang, Weizhi Ma +2
Recommendation model interpretation aims to reveal the relationships between inputs, model internal representations and outputs to enhance the transparency, interpretability, and t…
Beyond Utility: Evaluating LLM as Recommender
Chumeng Jiang, Jiayin Wang, Weizhi Ma +4
With the rapid development of Large Language Models (LLMs), recent studies employed LLMs as recommenders to provide personalized information services for distinct users. Despite ef…
PerSRV: Personalized Sticker Retrieval with Vision-Language Model
Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo +2
Instant Messaging is a popular means for daily communication, allowing users to send text and stickers. As the saying goes, "a picture is worth a thousand words", so developing an…
Large Language Models as Evaluators for Recommendation Explanations
Xiaoyu Zhang, Yishan Li, Jiayin Wang +4
The explainability of recommender systems has attracted significant attention in academia and industry. Many efforts have been made for explainable recommendations, yet evaluating…
ReChorus2.0: A Modular and Task-Flexible Recommendation Library
Jiayu Li, Hanyu Li, Zhiyu He +4
With the applications of recommendation systems rapidly expanding, an increasing number of studies have focused on every aspect of recommender systems with different data inputs, m…
To Recommend or Not: Recommendability Identification in Conversations with Pre-trained Language Models
Zhefan Wang, Weizhi Ma, Min Zhang
Most current recommender systems primarily focus on what to recommend, assuming users always require personalized recommendations. However, with the widely spread of ChatGPT and ot…
Aiming at the Target: Filter Collaborative Information for Cross-Domain Recommendation
Hanyu Li, Weizhi Ma, Peijie Sun +6
Cross-domain recommender (CDR) systems aim to enhance the performance of the target domain by utilizing data from other related domains. However, irrelevant information from the so…
Sequential Recommendation with Latent Relations based on Large Language Model
Shenghao Yang, Weizhi Ma, Peijie Sun +4
Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods r…
Common Sense Enhanced Knowledge-based Recommendation with Large Language Model
Shenghao Yang, Weizhi Ma, Peijie Sun +4
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performan…
A Situation-aware Enhancer for Personalized Recommendation
Jiayu Li, Peijie Sun, Chumeng Jiang +3
When users interact with Recommender Systems (RecSys), current situations, such as time, location, and environment, significantly influence their preferences. Situations serve as t…
Recommender for Its Purpose: Repeat and Exploration in Food Delivery Recommendations
Jiayu Li, Aixin Sun, Weizhi Ma +2
Recommender systems have been widely used for various scenarios, such as e-commerce, news, and music, providing online contents to help and enrich users' daily life. Different scen…
Intersectional Two-sided Fairness in Recommendation
Yifan Wang, Peijie Sun, Weizhi Ma +4
Fairness of recommender systems (RS) has attracted increasing attention recently. Based on the involved stakeholders, the fairness of RS can be divided into user fairness, item fai…
MACRec: a Multi-Agent Collaboration Framework for Recommendation
Zhefan Wang, Yuanqing Yu, Wendi Zheng +2
LLM-based agents have gained considerable attention for their decision-making skills and ability to handle complex tasks. Recognizing the current gap in leveraging agent capabiliti…
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems
Yuanqing Yu, Chongming Gao, Jiawei Chen +5
Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising attention for their potential to enhance long-term user engagement. However, research in this field f…
Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation
Shenghao Yang, Chenyang Wang, Yankai Liu +7
Item representation learning (IRL) plays an essential role in recommender systems, especially for sequential recommendation. Traditional sequential recommendation models usually ut…
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
Qingyao Ai, Ting Bai, Zhao Cao +30
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Mod…
Measuring Item Global Residual Value for Fair Recommendation
Jiayin Wang, Weizhi Ma, Chumeng Jiang +4
In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various rec…
Intent-aware Ranking Ensemble for Personalized Recommendation
Jiayu Li, Peijie Sun, Zhefan Wang +5
Ranking ensemble is a critical component in real recommender systems. When a user visits a platform, the system will prepare several item lists, each of which is generally from a s…
Towards Representation Alignment and Uniformity in Collaborative Filtering
Chenyang Wang, Yuanqing Yu, Weizhi Ma +4
Collaborative filtering (CF) plays a critical role in the development of recommender systems. Most CF methods utilize an encoder to embed users and items into the same representati…
A Survey on the Fairness of Recommender Systems
Yifan Wang, Weizhi Ma, Min Zhang +2
Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people's daily lives. Since recommendations involve allocation…