Year: 2025
Type: article
Abstract: : This study explores a new approach using Graph Neural Networks for session-based recommendation systems. These systems focus on forecasting user preferences by examining recent interactions instead of depending on past user profiles. Conventional approaches like collaborativefilteringandrecurrentneuralnetworksoftenfacechallengesinidentifyingcomplex sequential patterns and relationships between items within session data. Graph Neural Networks offer a promising alternative by representing session data as graphs, allowing them to efficiently capturebothlocalandglobaldependencies. This study seekstoevaluatetheeffectivenessofGNNs in improving session-based recommendation systems by enhancing prediction accuracy, scalability, and adaptability across diverse domains, including e-commerce, streaming services, andonlineeducation. ByharnessingthecapabilitiesofGraphNeuralNetworks,thesesystemscan gain deeper insights into item relationships, user behaviors, and contextual interactions, resulting in more precise recommendations. This paper examines the development of session-based recommendation systems, focusing on the transition from heuristic models to deep learning techniques. It also highlights the role of Graph Neural Networks in enhancing recommendation accuracy, scalability, and adaptability across various domains, including streaming services, e- commerce, and online education. Future innovations, such as hybrid models, self-supervised learning, and fairness-aware algorithms, offer promising opportunities to further improve performance and reduce biases in recommendation systems. Several real-world case studies highlight the effectiveness of Graph Neural Networks in session- based recommendation systems. In the e-commerce industry, platforms such as Alibaba have leveraged GNN-based models to analyze complex user-item interaction graphs, resulting in notable improvements in click-through and conversion rates. In the streaming sector, companies likeSpotifyandNetflixemployGNNstobettercapturedynamicuserpreferenceswithinsessions, enablingthedeliveryofmoreaccurateandtimelycontentrecommendations.Likewise,inthefieldofonlineeducation,platformssuchasCo urseraandedXutilizeGNNstorecommendpersonalized learning paths by modeling learners' interactions with courses and materials as graphs.
Authors: Neha Perween, Niraj Kumar Rai
Institution: Amity University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411112168