A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
Then, in order to better capture the complicated non-linearity of graph data, we present a novel GraphAIR framework which models the neighborhood interaction in addition to neighborhood aggregation. ... These GCNs generate node representation by aggregating features from the neighborhoods, which follows the "neighborhood aggregation" scheme. ... ACKNOWLEDGEMENTS This work is supported by National Natural Science Foundation of China (61772528) and National Key Research and Development Program (2018YFB1402600, 2016YFB1001000). ...arXiv:1911.01731v3 fatcat:q2zl42m2gfdxpoap5ye3uoq4hq
In this paper, we proposed a novel attentive knowledge graph attribute network(AKGAN) to learn item attributes and user interests via attribute information in KG. ... Knowledge Graphs (KGs) have shown great success in recommendation. This is attributed to the rich attribute information contained in KG to improve item and user representations as side information. ... KGAT applies an attentive neighborhood aggregation mechanism on a holistic graph and introduces TransR to regularize the representations. • KGNN-LS  : It uses a user-specific relation scoring function ...arXiv:2112.09425v1 fatcat:qej4svfjhbhfjmazhcowu5lboi