56 citations · 223 across the 16 of their papers we have counts for
6 papers · 1 filter
Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation
Ziqian Chen, Fei Sun, Yifan Tang +3
Recently, privacy issues in web services that rely on users' personal data have raised great attention. Unlike existing privacy-preserving technologies such as federated learning a…
CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation
Xu Xie, Zhaoyang Liu, Shiwen Wu +6
To improve user experience and profits of corporations, modern industrial recommender systems usually aim to select the items that are most likely to be interacted with (e.g., clic…
Explore User Neighborhood for Real-time E-commerce Recommendation
Xu Xie, Fei Sun, Xiaoyong Yang +4
Recommender systems play a vital role in modern online services, such as Amazon and Taobao. Traditional personalized methods, which focus on user-item (UI) relations, have been wid…
Contrastive Learning for Sequential Recommendation
Xu Xie, Fei Sun, Zhaoyang Liu +4
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 interactio…
Modeling Personalized Item Frequency Information for Next-basket Recommendation
Haoji Hu, Xiangnan He, Jinyang Gao +1
Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential mod…
Privileged Features Distillation at Taobao Recommendations
Chen Xu, Quan Li, Junfeng Ge +7
Features play an important role in the prediction tasks of e-commerce recommendations. To guarantee the consistency of off-line training and on-line serving, we usually utilize the…