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cs.IR2023
CDR: Conservative Doubly Robust Learning for Debiased Recommendation
ZiJie Song, JiaWei Chen, Sheng Zhou +4
In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. Consequently, tackling bias has emerged as a…
cs.AI2023
Jointly Complementary&Competitive Influence Maximization with Concurrent Ally-Boosting and Rival-Preventing
Qihao Shi, Wenjie Tian, Wujian Yang +3
In this paper, we propose a new influence spread model, namely, Complementary\&Competitive Independent Cascade (CIC) model. CIC model generalizes three well known influence…
cs.AI2023
Robust Sequence Networked Submodular Maximization
Qihao Shi, Bingyang Fu, Can Wang +4
In this paper, we study the \underline{R}obust \underline{o}ptimization for \underline{se}quence \underline{Net}worked \underline{s}ubmodular maximization (RoseNets) problem. We in…