103 citations · 330 across the 70 of their papers we have counts for
28 papers · 1 filter
Closing the Long-Short View Gap in Sequential Recommendation without Cached History
Lingfeng Shi, Chengkai Huang, Lina Yao +1
Sequential recommenders are typically trained on long user histories to capture rich behavioral signals, yet serving with training-length sequences is often impractical due to real…
DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management
Kai Yin, Xiangjue Dong, Chengkai Liu +4
Effective and efficient access to relevant information is essential for disaster management. However, no retrieval model is specialized for disaster management, and existing genera…
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management
Kai Yin, Xiangjue Dong, Chengkai Liu +5
Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on…
Towards An Efficient LLM Training Paradigm for CTR Prediction
Allen Lin, Renqin Cai, Yun He +5
Large Language Models (LLMs) have demonstrated tremendous potential as the next-generation ranking-based recommendation system. Many recent works have shown that LLMs can significa…
Federated Conversational Recommender System
Allen Lin, Jianling Wang, Ziwei Zhu +1
Conversational Recommender Systems (CRSs) have become increasingly popular as a powerful tool for providing personalized recommendation experiences. By directly engaging with users…
Flow Matching for Collaborative Filtering
Chengkai Liu, Yangtian Zhang, Jianling Wang +2
Generative models have shown great promise in collaborative filtering by capturing the underlying distribution of user interests and preferences. However, existing approaches strug…
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Weizhi Zhang, Yuanchen Bei, Liangwei Yang +15
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recomm…
ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning
Millennium Bismay, Xiangjue Dong, James Caverlee
This paper presents ReasoningRec, a reasoning-based recommendation framework that leverages Large Language Models (LLMs) to bridge the gap between recommendations and human-interpr…
TwinCL: A Twin Graph Contrastive Learning Model for Collaborative Filtering
Chengkai Liu, Jianling Wang, James Caverlee
In the domain of recommendation and collaborative filtering, Graph Contrastive Learning (GCL) has become an influential approach. Nevertheless, the reasons for the effectiveness of…
Behavior-Dependent Linear Recurrent Units for Efficient Sequential Recommendation
Chengkai Liu, Jianghao Lin, Hanzhou Liu +2
Sequential recommender systems aims to predict the users' next interaction through user behavior modeling with various operators like RNNs and attentions. However, existing models…
Countering Mainstream Bias via End-to-End Adaptive Local Learning
Jinhao Pan, Ziwei Zhu, Jianling Wang +2
Collaborative filtering (CF) based recommendations suffer from mainstream bias -- where mainstream users are favored over niche users, leading to poor recommendation quality for ma…
Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models
Chengkai Liu, Jianghao Lin, Jianling Wang +2
Sequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors. Although Transformer-based models have proven t…
Large Language Models as Data Augmenters for Cold-Start Item Recommendation
Jianling Wang, Haokai Lu, James Caverlee +2
The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation…
Automated Data Denoising for Recommendation
Yingqiang Ge, Mostafa Rahmani, Athirai Irissappane +3
In real-world scenarios, most platforms collect both large-scale, naturally noisy implicit feedback and small-scale yet highly relevant explicit feedback. Due to the issue of data…
Enhancing User Personalization in Conversational Recommenders
Allen Lin, Ziwei Zhu, Jianling Wang +1
Conversational recommenders are emerging as a powerful tool to personalize a user's recommendation experience. Through a back-and-forth dialogue, users can quickly hone in on just…
Evolution of Filter Bubbles and Polarization in News Recommendation
Han Zhang, Ziwei Zhu, James Caverlee
Recent work in news recommendation has demonstrated that recommenders can over-expose users to articles that support their pre-existing opinions. However, most existing work focuse…
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
Yin Zhang, Ruoxi Wang, Tiansheng Yao +5
Industry recommender systems usually suffer from highly-skewed long-tail item distributions where a small fraction of the items receives most of the user feedback. This skew hurts…
Towards Fair Conversational Recommender Systems
Allen Lin, Ziwei Zhu, Jianling Wang +1
Conversational recommender systems have demonstrated great success. They can accurately capture a user's current detailed preference -- through a multi-round interaction cycle -- t…
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems
Allen Lin, Jianling Wang, Ziwei Zhu +1
Conversational recommender systems (CRS) have shown great success in accurately capturing a user's current and detailed preference through the multi-round interaction cycle while e…
Evolution of Popularity Bias: Empirical Study and Debiasing
Ziwei Zhu, Yun He, Xing Zhao +1
Popularity bias is a long-standing challenge in recommender systems. Such a bias exerts detrimental impact on both users and item providers, and many efforts have been dedicated to…
Session-based Recommendation with Hypergraph Attention Networks
Jianling Wang, Kaize Ding, Ziwei Zhu +1
Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms. A critical challenge is to accurately model user in…
Sequential Recommendation for Cold-start Users with Meta Transitional Learning
Jianling Wang, Kaize Ding, James Caverlee
A fundamental challenge for sequential recommenders is to capture the sequential patterns of users toward modeling how users transit among items. In many practical scenarios, howev…
Fairness-aware Personalized Ranking Recommendation via Adversarial Learning
Ziwei Zhu, Jianling Wang, James Caverlee
Recommendation algorithms typically build models based on historical user-item interactions (e.g., clicks, likes, or ratings) to provide a personalized ranked list of items. These…
Understanding Car-Speak: Replacing Humans in Dealerships
Habeeb Hooshmand, James Caverlee
A large portion of the car-buying experience in the United States involves interactions at a car dealership. At the dealership, the car-buyer relays their needs to a sales represen…