21 citations · 61 across the 9 of their papers we have counts for
5 papers · 1 filter
State Regularized Policy Optimization on Data with Dynamics Shift
Zhenghai Xue, Qingpeng Cai, Shuchang Liu +4
In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of cu…
Constrained Reinforcement Learning for Short Video Recommendation
Qingpeng Cai, Ruohan Zhan, Chi Zhang +5
The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users provide complex and…
LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm
Meng Ai, Biao Li, Heyang Gong +5
Offering incentives (e.g., coupons at Amazon, discounts at Uber and video bonuses at Tiktok) to user is a common strategy used by online platforms to increase user engagement and p…
PASTO: Strategic Parameter Optimization in Recommendation Systems -- Probabilistic is Better than Deterministic
Weicong Ding, Hanlin Tang, Jingshuo Feng +17
Real-world recommendation systems often consist of two phases. In the first phase, multiple predictive models produce the probability of different immediate user actions. In the se…
Compositional Network Embedding
Tianshu Lyu, Fei Sun, Peng Jiang +2
Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existin…