126 citations · 244 across the 7 of their papers we have counts for
10 papers · 1 filter
RecAD: Towards A Unified Library for Recommender Attack and Defense
Changsheng Wang, Jianbai Ye, Wenjie Wang +3
In recent years, recommender systems have become a ubiquitous part of our daily lives, while they suffer from a high risk of being attacked due to the growing commercial and social…
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Chongming Gao, Kexin Huang, Jiawei Chen +6
Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice…
Vague Preference Policy Learning for Conversational Recommendation
Gangyi Zhang, Chongming Gao, Wenqiang Lei +6
Conversational recommendation systems (CRS) commonly assume users have clear preferences, leading to potential over-filtering of relevant alternatives. However, users often exhibit…
On the Theories Behind Hard Negative Sampling for Recommendation
Wentao Shi, Jiawei Chen, Fuli Feng +4
Negative sampling has been heavily used to train recommender models on large-scale data, wherein sampling hard examples usually not only accelerates the convergence but also improv…
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
Chongming Gao, Shijun Li, Yuan Zhang +5
Recommender systems deployed in real-world applications can have inherent exposure bias, which leads to the biased logged data plaguing the researchers. A fundamental way to addres…
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
Chongming Gao, Shiqi Wang, Shijun Li +6
While personalization increases the utility of recommender systems, it also brings the issue of filter bubbles. E.g., if the system keeps exposing and recommending the items that t…