273 citations · 1k across the 29 of their papers we have counts for
4 papers · 1 filter
Unbiased Knowledge Distillation for Recommendation
Gang Chen, Jiawei Chen, Fuli Feng +2
As a promising solution for model compression, knowledge distillation (KD) has been applied in recommender systems (RS) to reduce inference latency. Traditional solutions first tra…
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…
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
Chongming Gao, Shijun Li, Wenqiang Lei +6
The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive. This is…