Xin Xin

Self-Supervised Reinforcement Learning for Recommender Systems

2020 · arXiv (Cornell University)

In session-based or sequential recommendation, it is important to consider a number of factors like long-term user engagement, multiple types of user-item interactions such as clicks, purchases etc. The current state-of-the-art supervised approaches fail to …

Learning Robust Recommenders through Cross-Model Agreement

2022 · Proceedings of the ACM Web Conference 2022

Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while negative examples are sampled from uninteracted ones. However, noisy …

Choosing the Best of Both Worlds

2022 · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms. However, by focusing on item relevance, one …

Relational Collaborative Filtering

2019

Existing item-based collaborative filtering (ICF) methods leverage only the relation of collaborative similarity - i.e., the item similarity evidenced by user interactions like ratings and purchases. Nevertheless, there exist multiple relations between items in real-world …

AutoDebias: Learning to Debias for Recommendation

2021

Recommender systems rely on user behavior data like ratings and clicks to build personalization model. However, the collected data is observational rather than experimental, causing various biases in the data which significantly affect the learned …