2 citations · 2 across the 7 of their papers we have counts for
7 papers
Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution
Shengjia Zhang, Jiawei Chen, Changdong Li +5
Loss functions play a pivotal role in optimizing recommendation models. Among various loss functions, Softmax Loss (SL) and Cosine Contrastive Loss (CCL) are particularly effective…
Distributionally Robust Graph-based Recommendation System
Bohao Wang, Jiawei Chen, Changdong Li +6
With the capacity to capture high-order collaborative signals, Graph Neural Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their efficacy of…
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…
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…
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…
SamWalker++: recommendation with informative sampling strategy
Can Wang, Jiawei Chen, Sheng Zhou +3
Recommendation from implicit feedback is a highly challenging task due to the lack of reliable negative feedback data. Existing methods address this challenge by treating all the u…
Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback
Jiawei Chen, Can Wang, Sheng Zhou +4
Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendat…