research-article

A graph neural approach for group recommendation system based on pairwise preferences

Authors:

Roza Abolghasemi,

Enrique Herrera Viedma,

Paal Engelstad,

Youcef Djenouri, and

Anis YazidiAuthors Info & Claims

Volume 107, Issue C

https://doi.org/10.1016/j.inffus.2024.102343

Published: 02 July 2024 Publication History

Abstract

Pairwise preference information, which involves users expressing their preferences by comparing items, plays a crucial role in decision-making and has recently found application in recommendation systems. In this study, we introduce GcPp, a clustering algorithm that leverages pairwise preference data to generate recommendations for user groups. Initially, we construct individual graphs for each user based on their pairwise preferences and utilize a graph convolutional network to predict similarities between all pairs of graphs. These predicted similarity scores form the foundation of our research. We then construct a new graph where users are nodes and the edges are weighted according to the predicted similarities. Finally, we perform clustering on the graph’s nodes (users). By evaluating various metrics, we found that employing a similarity metric based on a convolutional neural network (SimGNN) with our proposed ground truth called Top-K yielded the highest accuracy. The proposed approach is specifically designed for group recommendation systems and holds significant potential for group decision-making problems. Code is available at https://github.com/RozaAbolghasemi/Group_Recommendation_Syatem_GcPp_clustering.

Highlights

•

For precise and fair group recommendations, one can use clusters of similar users as groups.

•

The introduced GcPp is a Graph Clustering method based on Pairwise Preferences data.

•

User similarity is determined by shared preferences, diverging from conventional feature vectors.

•

Similarity prediction using both overall user preference (Top-K) and detailed pairwise preferences.

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cover image Information Fusion

Information Fusion Volume 107, Issue C

Jul 2024

522 pages

ISSN:1566-2535

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Elsevier Science Publishers B. V.

Netherlands

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Published: 02 July 2024

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