Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10671
Title: Social and Categorical Signals in Contrastive Learning for Recommendation Systems
Authors: Dergi, Halil Berk
Akgun, Mehmet Burak
Keywords: Information Systems
Recommender Systems
Social Recomendation
Graph Neural Network
Issue Date: 2023
Publisher: IEEE
Abstract: Social recommendation systems that use graph neural network (GNN) models are effective in addressing the data sparsity issue present in collaborative filtering models. Social homophily and item similarities are critical factors that affect users' preferences, and GNN models must capture these factors while incorporating users' interaction behaviors. In this work, we propose SCCL, a recommendation model that jointly captures social influence and item similarity signals with cross-view contrastive learning. We constructed a user-user social graph from social networks and item-item graphs from common tags. In our model, user-user relations are represented as a homogeneous graph, and item-item relations are represented as hypergraphs. We demonstrate the effectiveness of our model on two real-world datasets.
Description: 31st IEEE Conference on Signal Processing and Communications Applications (SIU) -- JUL 05-08, 2023 -- Istanbul Tech Univ, Ayazaga Campus, Istanbul, TURKEY
URI: https://doi.org/10.1109/SIU59756.2023.10223958
https://hdl.handle.net/20.500.11851/10671
ISBN: 979-8-3503-4355-7
ISSN: 2165-0608
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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