Liang Chen

TL;DR: Moving semantic transfer network is presented, which learn semantic representations for unlabeled target samples by aligning labeled source centroid and pseudo-labeled target centroid, resulting in an improved target classification accuracy.

TL;DR: This work explores self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation, and implements it on the state-of-the-art model LightGCN, which has the ability of automatically mining hard negatives.

TL;DR: Mapping out the challenges and strategies for the widespread adoption of service computing shows clear trends in adoption and a clear road map for the future direction is proposed.

TL;DR: A neighborhood-based collaborative filtering approach to predict such unknown values for QoS-based selection and has three new features: the adjusted-cosine-based similarity calculation to remove the impact of different QoS scale; a data smoothing process to improve prediction accuracy; and a similarity fusion approach to handle the data sparsity problem.

TL;DR: Wu et al. as discussed by the authors explored self-supervised learning on user-item graph, so as to improve the accuracy and robustness of graph convolutional networks for recommendation.