674 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of Illinois ChicagoUS13 papers
- Singapore Management UniversitySG11 papers
- Carnegie Mellon UniversityUS8 papers
- Georgia Institute of TechnologyUS7 papers
- Princeton UniversityUS7 papers
- Nanyang Technological UniversitySG6 papers
- University of Illinois Urbana-ChampaignUS6 papers
- Agency for Science, Technology and ResearchSG5 papers
- Chinese University of Hong KongHK5 papers
- University of Maryland, College ParkUS5 papers
- University of WashingtonUS5 papers
- Beihang UniversityCN4 papers
12 papers · 1 filter
Bias-Aware Agent: Enhancing Fairness in AI-Driven Knowledge Retrieval
Karanbir Singh, William Ngu
Advancements in retrieving accessible information have evolved faster in the last few years compared to the decades since the internet's creation. Search engines, like Google, have…
Training Large Recommendation Models via Graph-Language Token Alignment
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Recommender systems (RS) have become essential tools for helping users efficiently navigate the overwhelming amount of information on e-commerce and social platforms. However, trad…
Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems
Yuwei Cao, Liangwei Yang, Zhiwei Liu +5
Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address thi…
Graph-based Alignment and Uniformity for Recommendation
Liangwei Yang, Zhiwei Liu, Chen Wang +4
Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hy…
Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
With the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the group identifi…
Dynamic Causal Collaborative Filtering
Shuyuan Xu, Juntao Tan, Zuohui Fu +3
Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loo…
Mining Root Cause Knowledge from Cloud Service Incident Investigations for AIOps
Amrita Saha, Steven C. H. Hoi
Root Cause Analysis (RCA) of any service-disrupting incident is one of the most critical as well as complex tasks in IT processes, especially for cloud industry leaders like Salesf…
Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network
Liangwei Yang, Zhiwei Liu, Yu Wang +3
Because of the large number of online games available nowadays, online game recommender systems are necessary for users and online game platforms. The former can discover more pote…
RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems
Zohreh Ovaisi, Shelby Heinecke, Jia Li +3
Robust machine learning is an increasingly important topic that focuses on developing models resilient to various forms of imperfect data. Due to the pervasiveness of recommender s…
Deconfounded Causal Collaborative Filtering
Shuyuan Xu, Juntao Tan, Shelby Heinecke +2
Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation pe…
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
Zhiwei Liu, Yongjun Chen, Jia Li +3
Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. At their core, such approache…
CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
Andre Esteva, Anuprit Kale, Romain Paulus +4
The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related…