Bin Cui

OneSketch: A Generic and Accurate Sketch for Data Streams

2023 · IEEE Transactions on Knowledge and Data Engineering

In this paper, we propose a generic sketch algorithm capable of achieving more accuracy in the following five tasks: finding top-$k$frequent items, finding heavy hitters, per-item frequency estimation, and heavy changes in the time and …

Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning

2023

Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges. One major …

Mitigating Negative Transfer in Cross-Domain Recommendation via Knowledge Transferability Enhancement

2024

Cross-Domain Recommendation (CDR) is a promising technique to alleviate data sparsity by transferring knowledge across domains. However, the negative transfer issue in the presence of numerous domains has received limited attention. Most existing methods transfer …

Adapting to User Interest Drift for POI Recommendation

2016 · IEEE Transactions on Knowledge and Data Engineering

Point-of-Interest recommendation is an essential means to help people discover attractive locations, especially when people travel out of town or to unfamiliar regions. While a growing line of research has focused on modeling user geographical …

Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation

2016 · ACM Transactions on Information Systems

Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting places, especially when users travel out of town. However, the extreme sparsity of a user-POI matrix creates a severe challenge. …

Graph Neural Networks in Recommender Systems: A Survey

2022 · ACM Computing Surveys

With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this …

Contrastive Learning for Sequential Recommendation

2022 · 2022 IEEE 38th International Conference on Data Engineering (ICDE)

Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical inter-actions. Despite their success, we argue that these approaches usually rely …