Session Based Recommender System Using Gated Graph Neural Network

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,platformssuchasCourseraandedXutilizeGNNstorecommendpersonalized learning paths by modeling learners\' interactions with courses and materials as graphs.

Introduction

Session-based recommendation systems predict user preferences based on recent activity rather than long-term history. Traditional models like collaborative filtering and content-based approaches struggle with sequential dependencies and sparse data. Deep learning methods—especially Graph Neural Networks (GNNs)—have emerged as powerful alternatives due to their ability to model session data as structured graphs and capture complex item relationships.

Key Contributions:

1. Graph Neural Networks (GNNs) for Session Recommendations:

• GNNs use message-passing to capture both short- and long-term dependencies between items in a session.

• Outperform RNNs and traditional models in accuracy, personalization, and scalability.

• Capable of modeling user behavior in dynamic, sparse, and real-time environments.

2. Transformer-based GNN Architecture:

• Combines GNN structure with the attention mechanism from Transformers.

• Enables the model to focus on important neighbors in the session graph.

• Supports more expressive and context-aware recommendations.

Study Objectives:

1. Trace the evolution of session-based recommendation systems from heuristics to deep learning.

2. Explore Gated Graph Neural Networks (GGNNs) for sequential modeling in sessions.

3. Address cold-start and data sparsity challenges.

4. Benchmark GNNs against CF, RNNs, and Transformer-based models.

5. Examine ethical aspects like fairness, transparency, and privacy.

Literature Insights:

• Neural Matrix Factorization: Improves upon linear models like matrix factorization.

• GGNNs: Model item sequences and trends effectively.

• Cold-start Mitigation: GNNs leverage structured and unstructured auxiliary information.

• Pretraining & Heterogeneous Graphs: Offer new opportunities but introduce complexity.

• Collaborative Attention: Improves recommendation relevance by focusing on session-specific item relationships.

Methodology:

• Data Sources: Literature reviews and empirical case studies in e-commerce, streaming, and online education.

• Evaluation: Compared GNN-based models with CF, RNNs, and Transformer models.

• Metrics: Focused on prediction accuracy, cold-start handling, scalability, and personalization.

Key Findings:

A. Superior Sequential Modeling

• GNNs capture complex item transitions and user behavior patterns.

B. Cold Start Handling

• Graph structures help infer preferences for new users/items using neighborhood aggregation.

C. Hybrid Personalization

• Combining GNNs with CF/CBF enhances personalization by balancing short-term and long-term preferences.

D. Context-Aware Recommendations

• Session dynamics and temporal context improve relevance and user engagement.

E. Ethical Considerations

• Emphasizes fairness, debiasing, and transparency in recommendations.

• Encourages responsible AI through explainability and privacy safeguards.

Future Directions:

A. Emerging Technologies

• Efficient GNNs: Scalable architectures for large-scale data.

• Hybrid Models: Integration with Transformers and reinforcement learning for adaptive recommendations.

• Federated Learning: Enables privacy-preserving personalization without sharing raw user data.

B. Challenges

• Bias & Fairness: Need algorithms to mitigate unintended discrimination.

• Regulatory Compliance: Systems must align with privacy laws (e.g., GDPR, CCPA) using techniques like differential privacy.

Conclusion

To sum up, this study has provided a comprehensive analysis of the effectiveness and transformative potential of Graph Neural Network (GNN)-based session-based recommendation systems. A systematic exploration of past developments, current implementations, and future directionshasrevealedseveralkeyinsights.First,GNN-basedmodelshavesignificantlyenhanced the accuracy, efficiency, and adaptability of session-based recommendation systems. Byleveraginggraphstructurestomodelcomplexuseriteminteractions,thesesystemsoutperformtraditionalapproachessuchascollaborativefilteringandrecurrentneuralnetworks(RNNs).Their ability to capture sequential dependencies and contextual relationships has led to improved recommendation quality across various domains, including e-commerce, media streaming, and online learning. Additionally, the integration of graph neural networks into recommendation systems highlights the convergence of AI-driven innovation and real-world applications. The dynamic interaction between deep learning techniques and user behavior modeling has reshaped conventional recommendation frameworks, enabling personalized experiences and adaptive learning capabilities. The findings of this study have broad implications for researchers, practitioners, and industry leaders. Organizations adopting GNN-based recommendation models can enhance user engagement,optimizecontentdelivery, anddrivebusinessgrowth. Atthesametime,dataprivacy, fairness, and ethical considerations remain crucial aspects of responsible AI deployment. Addressing bias mitigation, interpretability, and regulatory compliance will be essential to ensuring that AI-driven recommendation systems benefit users equitably and transparently. Policymakers must take proactive steps to regulate the ethical deployment of AI-powered recommendations, ensuringthatuserprivacy,fairness,andaccountabilityaremaintained.Industry collaboration, multidisciplinary research, and regulatory frameworks will play a key role in balancing technological innovation with ethical responsibility. In conclusion, Graph NeuralNetwork-based recommendationsystemshaveimmensepotentialto revolutionizesession-based recommendationsacrossmultiplesectors.Byfosteringcollaboration,continuouslearning,andethicalAIpractices,stakeholderscanharnessthefullpotentialofGNNs to drive innovation, enhance user experiences, and shape the future of personalized recommendation technologies.

References

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Copyright

Copyright © 2025 Neha Perween, Niraj Kumar Rai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.