Depeng Jin

Beyond K-Anonymity: Protect Your Trajectory from Semantic Attack

2017

Nowadays, human trajectories are widely collected and utilized for scientific research and business purpose. However, publishing trajectory data without proper handling might cause severe privacy leakage. A large body of works are dedicated to merging …

Mixed Attention Network for Cross-domain Sequential Recommendation

2023 · arXiv (Cornell University)

In modern recommender systems, sequential recommendation leverages chronological user behaviors to make effective next-item suggestions, which suffers from data sparsity issues, especially for new users. One promising line of work is the cross-domain recommendation, which …

Sampler Design for Bayesian Personalized Ranking by Leveraging View Data

2019 · IEEE Transactions on Knowledge and Data Engineering

Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR depends largely on the quality of negative sampler. In this paper, we …

Reinforced Negative Sampling for Recommendation with Exposure Data

2019

In implicit feedback-based recommender systems, user exposure data, which record whether or not a recommended item has been interacted by a user, provide an important clue on selecting negative training samples. In this work, we …

Multi-behavior Recommendation with Graph Convolutional Networks

2020

Traditional recommendation models that usually utilize only one type of user-item interaction are faced with serious data sparsity or cold start issues. Multi-behavior recommendation taking use of multiple types of user-item interactions, such as clicks …

Disentangling User Interest and Conformity for Recommendation with Causal Embedding

2021

Recommendation models are usually trained on observational interaction data. However, observational interaction data could result from users’ conformity towards popular items, which entangles users’ real interest. Existing methods tracks this problem as eliminating popularity bias, …

Sequential Recommendation with Graph Neural Networks

2021

Sequential recommendation aims to leverage users' historical behaviors to predict their next interaction. Existing works have not yet addressed two main challenges in sequential recommendation. First, user behaviors in their rich historical sequences are often …

Disentangling Long and Short-Term Interests for Recommendation

2022 · Proceedings of the ACM Web Conference 2022

Modeling user’s long-term and short-term interests is crucial for accurate recommendation. However, since there is no manually annotated label for user interests, existing approaches always follow the paradigm of entangling these two aspects, which may …

A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions

2023 · ACM Transactions on Recommender Systems

Recommender system is one of the most important information services on today’s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of …