Abstract
Graph Convolutional Networks (GCNs) have emerged as a hot topic of interest for collaborative filtering among researchers in the recent past. The research which exists in literature and is applied to recommendation does not analyze all the facets of GCN, as GCN is introduced for graph classification activities. It is observed that the two facets of GCNs namely, feature transformation and non-linear activation have a small influence on increasing the effectiveness of collaborative filtering (CF). Furthermore, the inclusion of these two facets increases the complexity of training and even decreases the recommendation performance. In this paper, a novel approach namely Improved Graph Convolutional Network (ImprovedGCN) has been proposed which only makes use of the important part of GCN termed neighborhood aggregation for CF. The aforesaid model can be implemented and trained which leads to significant improvements as compared to a similar approach termed Neural Graph Collaborative Filtering (NGCF).
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.