Smarter Recommendations with Attention: A Survey of Recommendation Systems and the Graph Attention Technique

Sandhya M; Hanith Cg

doi:10.36948/ijfmr.2026.v08i01.64739

Smarter Recommendations with Attention: A Survey of Recommendation Systems and the Graph Attention Technique

Author(s)	Prof. Sandhya M, Mr. Hanith Cg
Country	India
Abstract	Recommendation systems is the core of most online experiences, guiding people to the right products, films, music, courses, and even social links when the choices are overwhelming. They work by learning from large interaction logs and applying predictive models to surface content that fits each user’s tastes, steadily improving as algorithms and data grow richer. As AI and deep learning have developed, classic strategies like collaborative filtering and content-based filtering are increasingly paired with, or superseded by, neural architectures that can model more nuanced patterns in behaviour and content. Among these neural approaches, graph neural networks—and especially Graph Attention Networks (GATs)—have become influential for recommendation because they treat users and items as nodes in a graph and learn from the connections between them. With attention mechanisms, GATs decide which neighbours matter most for a given prediction, amplifying useful signals and dampening noise to learn stronger user and item representations. This survey traces how the industry moved from traditional methods to graph-based models centred on GATs, comparing approaches on scalability, ranking accuracy, data sparsity, and interpretability across widely used benchmarks. Drawing from recent peer‑reviewed work, it highlights architectures and training practices that consistently help in real-world settings, as well as gaps that still limit robust deployment at scale. Looking forward, several directions stand out: using explainable AI to clarify why specific items are recommended, combining reinforcement learning with GATs for adaptive policies, and adopting federated learning to protect user privacy while training on distributed data. The versatility of GAT-based recommenders is already visible across domains—from e‑commerce and online learning to healthcare and social feeds—where graph structure and selective attention naturally fit the data. Overall, the evidence points to GATs as a key ingredient for the next generation of personalized, context‑aware, and trustworthy recommendation systems. Keywords: Recommendation Systems, Collaborative Filtering, Graph Neural Networks, Graph Attention Networks
Keywords	Recommendation Systems, Collaborative Filtering, Graph Neural Networks, Graph Attention Networks
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-05
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.64739
Short DOI	https://doi.org/hbh545

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Smarter Recommendations with Attention: A Survey of Recommendation Systems and the Graph Attention Technique

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