4.3k citations · 10.2k across the 68 of their papers we have counts for
6 papers · 2 filters
Latent User Intent Modeling for Sequential Recommenders
Bo Chang, Alexandros Karatzoglou, Yuyan Wang +3
Sequential recommender models are essential components of modern industrial recommender systems. These models learn to predict the next items a user is likely to interact with base…
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations
Flavien Prost, Ben Packer, Jilin Chen +9
There has been a flurry of research in recent years on notions of fairness in ranking and recommender systems, particularly on how to evaluate if a recommender allocates exposure e…
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
Yin Zhang, Ruoxi Wang, Tiansheng Yao +5
Industry recommender systems usually suffer from highly-skewed long-tail item distributions where a small fraction of the items receives most of the user feedback. This skew hurts…
Reward Shaping for User Satisfaction in a REINFORCE Recommender
Konstantina Christakopoulou, Can Xu, Sai Zhang +10
How might we design Reinforcement Learning (RL)-based recommenders that encourage aligning user trajectories with the underlying user satisfaction? Three research questions are key…
Learning to Augment for Casual User Recommendation
Jianling Wang, Ya Le, Bo Chang +3
Users who come to recommendation platforms are heterogeneous in activity levels. There usually exists a group of core users who visit the platform regularly and consume a large bod…
Recency Dropout for Recurrent Recommender Systems
Bo Chang, Can Xu, Matthieu Lê +5
Recurrent recommender systems have been successful in capturing the temporal dynamics in users' activity trajectories. However, recurrent neural networks (RNNs) are known to have d…