Zhaopeng Qiu

CBR: Context Bias aware Recommendation for Debiasing User Modeling and Click Prediction

2022 · Proceedings of the ACM Web Conference 2022

With the prosperity of recommender systems, the biases existing in user behaviors, which may lead to inconsistency between user preference and behavior records, have attracted wide attention. Though large efforts have been made to infer …

Distributionally Robust Sequential Recommnedation

2023

Modeling user sequential behaviors have been demonstrated to be effective in promoting the recommendation performance. While previous work has achieved remarkable successes, they mostly assume that the training and testing distributions are consistent, which may …

U-BERT: Pre-training User Representations for Improved Recommendation

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Learning user representation is a critical task for recommendation systems as it can encode user preference for personalized services. User representation is generally learned from behavior data, such as clicking interactions and review comments. However, …