A scalable deep learning-based context-aware recommendation framework using autoencoder-regularized neural interaction modeling
Md Mahtab Alammahtab.alam57@gmail.comDepartment of Computer EngineeringJamia Millia IslamiaJamia Nagar, New Delhi, Delhi, 110025, IndiaView full profile → , Mumtaz Ahmedmahmed1@jmia.ac.inDepartment of Computer EngineeringJamia Millia IslamiaJamia Nagar, New Delhi, Delhi, 110025, IndiaView full profile → , *Wakar AhmadCorresponding authorwaqar.ahmad50@gmail.comDepartment of Computer Science and EngineeringIndian Institute of Information Technology SonepatSonepat, Haryana, 131001, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Dec 2025
- Published Online:
- 31 Jul 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2329
- Pages:
- 2757–2770
Abstract
Recommendation systems are needed to handle large-scale user-item interactions on digital platforms, but they often face severe data sparsity and cold-start issues. Current collaborative filtering and deep-learning models are frequently oblivious to complex contextual dependencies and difficult to scale computationally. To address these limitations, the paper presents a context-sensitive recommendation framework built on deep learning that integrates autoencoder-based latent feature generation with a neural interaction model. The proposed model effectively captures high-order user-item-context correlations in a sparse environment and maintains computational efficiency suitable for large-scale deployment. Contextual cues, including time, category, and geographic ones, are combined through a hybrid manifold that optimizes reconstruction and prediction losses simultaneously. The model consistently outperforms state-of-the-art baselines by up to 15% in Recall@10 and NDCG@10 across three benchmark datasets (MovieLens-1M, Amazon, and Yelp), showing greater robustness in cold-start and high-sparsity situations. The implementation demonstrates effective GPU scalability, affirming the potential of the proposed framework as a robust and computationally feasible solution for real-world intelligent recommendation systems.
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References
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