Enhancing Real-Time Session-Based Recommendation System using Light Graph Convolutional Network

doi:10.23940/ijpe.25.07.p5.392400

Abstract

Abstract: A session-based recommender system (SBRS) focuses on users' interests depending on their browsing habits to make appropriate recommendations in a working session. Nowadays, graph neural networks are a very popular approach for the Session-based Recommendation System. In existing works, a session is treated as a single time-point moving user model and avoids the complex association of the items. Moreover, the temporal component of user representation learning is disregarded by the conventional graph convolutional network (GCN); the resulting user preference model is static and unable to capture the dynamism of user preferences. Also, their works overlook the time factor. In this study, we present a time-aware Session-Based Lightweight Graph Convolutional network (SB-LGCN) that employs several GNN techniques to effectively capture both static and dynamic user preferences and minimize the complexity. Our goal is to streamline GCN's design to make it more succinct and suitable for recommendation. The Proposed Light Graph Convolutional Network (Light GCN) model includes only the most essential component in GCN neighborhood aggregation. It learns user and item embeddings by linearly propagating them on the user-item interaction graph and uses the normalized sum of the neighbors' embeddings learned at all the layers as the final embedding. Finally, a weighted sum aggregator is used to achieve the prediction. The performance is verified by extensive experiments on the SB-LGCN model for the Movie Lens and YooChoose1/64 datasets. Results indicated that the proposed model outperforms the best by accuracy with impressive training time efficiency.

Key words: session, recommendation systems, embeddings, aggregation, light graph convolution network

Somen Roy, Jyothi Pillai, and Ani Thomas. Enhancing Real-Time Session-Based Recommendation System using Light Graph Convolutional Network [J]. Int J Performability Eng, 2025, 21(7): 392-400.

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