Item Graph Convolution Collaborative Filtering for Inductive Recommendations
Authors: 
Edoardo D’Amico, Khalil Muhammad, Elias Tragos, 
Barry Smyth, Neil Hurley, Aonghus LawlorAuthors Info & Claims
Edoardo D’Amico, Khalil Muhammad, Elias Tragos, Barry Smyth, Neil Hurley, Aonghus LawlorAuthors Info & ClaimsPages 249 - 263
Abstract
Graph Convolutional Networks (GCN) have been recently employed as core component in the construction of recommender system algorithms, interpreting user-item interactions as the edges of a bipartite graph. However, in the absence of side information, the majority of existing models adopt an approach of randomly initialising the user embeddings and optimising them throughout the training process. This strategy makes these algorithms inherently transductive, curtailing their ability to generate predictions for users that were unseen at training time. To address this issue, we propose a convolution-based algorithm, which is inductive from the user perspective, while at the same time, depending only on implicit user-item interaction data. We propose the construction of an item-item graph through a weighted projection of the bipartite interaction network and to employ convolution to inject higher order associations into item embeddings, while constructing user representations as weighted sums of the items with which they have interacted. Despite not training individual embeddings for each user our approach achieves state-of-the-art recommendation performance with respect to transductive baselines on four real-world datasets, showing at the same time robust inductive performance.
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Published In
Apr 2023
780 pages
ISBN:978-3-031-28243-0
DOI:10.1007/978-3-031-28244-7
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 02 April 2023
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