86 citations · 359 across the 27 of their papers we have counts for
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cs.IR2022★ 2 cited
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…
cs.IR2022
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…
cs.IR2022★ 1 cited
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…