A Dynamic Session-Based Recommendation System with Graph Neural Networks

Vrushali Mahajan*, Ermiyas Birihanu, Tsegaye Misikir Tashu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Session-based recommendation systems provide personalized recommendations to users based on their session activities. Traditional recommendation algorithms often overlook temporal dependencies within user sessions, leading to suboptimal recommendations. To address this limitation, we proposed a Temporal Graph Neural Network (TemporalGNN) approach that leverages the temporal relationships between items in sessions to enhance recommendations. The proposed session-based recommendation system can effectively capture temporal dependencies within user sessions. The method consists of two temporal GNN layers designed to capture temporal dependencies within user sessions. A directed graph is constructed from session data, where each unique item in the dataset is a node, and directed edges are created between consecutive items within a session, with edge weights representing the time differences between interactions. This graph structure allows the model to capture the sequential nature of user interactions. The model generates recommendations by computing similarity scores from the learned embeddings and selecting the top N items with the highest scores. The experimental results on two real-world datasets showed the effectiveness of the proposed method in improving recommendation performance compared to the baseline approaches. The source code implementation is available on the GitHub repository at https://github.com/VrushaliM/SB-Recommendation-GNN.

Original languageEnglish
Title of host publicationInformation Technologies – Applications and Theory 2024
Subtitle of host publicationProceedings of the 24th Conference Information Technologies – Applications and Theory (ITAT 2024)
EditorsLucie Ciencialová, Martin Holeňa, Róbert Jajcay, Tatiana Jajcayová, Martin Mačaj, František Mráz , Richard Ostertág, Dana Pardubsk, Martin Plátek, Martin Stanek
PublisherCEUR Workshop Proceedings
Pages31-36
Number of pages6
Publication statusPublished - 16-Oct-2024
Event24th Conference Information Technologies - Applications and Theory, ITAT 2024 - Drienica, Slovakia
Duration: 20-Sept-202424-Sept-2024

Publication series

NameCEUR Workshop Proceedings
Volume3792
ISSN (Print)1613-0073

Conference

Conference24th Conference Information Technologies - Applications and Theory, ITAT 2024
Country/TerritorySlovakia
CityDrienica
Period20/09/202424/09/2024

Keywords

  • Graph Neural Networks
  • Recommendation Systems
  • Session
  • User

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