Graph Convolutional Embeddings for Recommender Systems

Graph Convolutional Embeddings for Recommender Systems

Modern recommender systems (RS) work by processing a number of signals that can be inferred from large sets of user-item interaction data. The main signal to analyze stems from the raw matrix that represents interactions. However, we can increase the performance of RS by considering other kinds of s...

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Bibliographic Details
Main Authors: Paula G. Duran, Alexandros Karatzoglou, Jordi Vitria, Xin Xin, Ioannis Arapakis
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Graph convolutional network
interaction context
embedding layer
factorization machines
neural networks
collaborative filtering
Online Access:https://ieeexplore.ieee.org/document/9481221/

Internet

https://ieeexplore.ieee.org/document/9481221/

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