Graph Convolutional Embeddings for Recommender Systems

doi:10.1109/ACCESS.2021.3096609

Graph Convolutional Embeddings for Recommender Systems

2021-01-01

8

OA

AI

P

Paula Gómez Duran *

A

Alexandros Karatzoglou

DOI：10.1109/ACCESS.2021.3096609

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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 signals like the context of interactions, which could be, for example, the time or date of the interaction, the user location, or sequential data corresponding to the historical interactions of the user with the system. These complex, context-based interaction signals are characterized by a rich relational structure that can be represented by a multi-partite graph. Graph Convolutional Networks (GCNs) have been used successfully in collaborative filtering with simple user-item interaction data. In this work, we generalize the use of GCNs for N-partite graphs by considering N multiple context dimensions and propose a simple way for their seamless integration in modern deep learning RS architectures. More specifically, we define a graph convolutional embedding layer for N-partite graphs that processes user-item-context interactions and constructs node embeddings by leveraging their relational structure. Experiments on several datasets show the benefits of the introduced GCN embedding layer by measuring the performance of different context-enriched tasks.

Keywords:

Convolution

Frequency modulation

Context modeling

Collaboration

Tensors

Data models

Task analysis

Graph convolutional network

interaction context

embedding layer

factorization machines

neural networks

collaborative filtering

context-aware recommendation

Graph Convolutional Embeddings for Recommender Systems

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