1.5k citations · 3.6k across the 48 of their papers we have counts for
11 papers · 1 filter
OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation
Haoyang Fang, Shuai Zhang, Yifei Ma +5
Domain-specific finetuning is essential for dense retrievers, yet not all data pairs contribute equally to the learning process. We introduce OPERA, a data pruning framework that e…
A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms
Chengkai Huang, Hongtao Huang, Tong Yu +6
Recommender systems (RS) have become essential in filtering information and personalizing content for users. RS techniques have traditionally relied on modeling interactions betwee…
Toward Holistic Evaluation of Recommender Systems Powered by Generative Models
Yashar Deldjoo, Nikhil Mehta, Maheswaran Sathiamoorthy +3
Recommender systems powered by generative models (Gen-RecSys) extend beyond classical item ranking by producing open-ended content, which simultaneously unlocks richer user experie…
Foundation Models for Recommender Systems: A Survey and New Perspectives
Chengkai Huang, Tong Yu, Kaige Xie +3
Recently, Foundation Models (FMs), with their extensive knowledge bases and complex architectures, have offered unique opportunities within the realm of recommender systems (RSs).…
Learning User Representations with Hypercuboids for Recommender Systems
Shuai Zhang, Huoyu Liu, Aston Zhang +6
Modeling user interests is crucial in real-world recommender systems. In this paper, we present a new user interest representation model for personalized recommendation. Specifical…
Quaternion Collaborative Filtering for Recommendation
Shuai Zhang, Lina Yao, Lucas Vinh Tran +2
This paper proposes Quaternion Collaborative Filtering (QCF), a novel representation learning method for recommendation. Our proposed QCF relies on and exploits computation with Qu…
DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
Shuai Zhang, Yi Tay, Lina Yao +2
Deep learning based recommender systems have been extensively explored in recent years. However, the large number of models proposed each year poses a big challenge for both resear…
HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
Lucas Vinh Tran, Yi Tay, Shuai Zhang +2
This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic spa…
NeuRec: On Nonlinear Transformation for Personalized Ranking
Shuai Zhang, Lina Yao, Aixin Sun +3
Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relat…
Deep Learning based Recommender System: A Survey and New Perspectives
Shuai Zhang, Lina Yao, Aixin Sun +1
With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems ca…
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders
Shuai Zhang, Lina Yao, Xiwei Xu
Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matri…