Tong Chen

AIR: Attentional Intention-Aware Recommender Systems

2019

The capability of extracting sequential patterns from the user-item interaction data is now becoming a key feature of recommender systems. Though it is important to capture the sequential effect, existing methods only focus on modelling …

Graph Embedding for Recommendation against Attribute Inference Attacks

2021

In recent years, recommender systems play a pivotal role in helping users identify the most suitable items that satisfy personal preferences. As user-item interactions can be naturally modelled as graph-structured data, variants of graph convolutional …

Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation

2024

In Location-based Social Networks (LBSNs), Point-of-Interest (POI) recommendation helps users discover interesting places. There is a trend to move from the conventional cloud-based model to on-device recommendations for privacy protection and reduced server reliance. Due …

Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long …

Are Graph Augmentations Necessary?

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling …