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<p>This paper presents a hybrid movie recommendation framework that integrates Deep Spiking Neural Networks (DSNN) with Collaborative Filtering (CF). CF is utilized to preprocess and complete sparse user–movie rating matrices, which are then used to train the DSNN for efficient and robust classification. The event-driven nature of DSNN enables lower computational cost while maintaining high predictive performance. After evaluating the MovieLens dataset, the proposed DSNN-CF model outperforms traditional machine learning and deep learning baselines in terms of precision, recall, and F1-score, achieving an average improvement of 10% in prediction accuracy for recommending movies of interest.</p>

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A neuro-inspired hybrid framework for personalized movie recommendation using deep spiking neural networks and collaborative filtering

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Shiba Prasad Dash,
  • Sambit Prusty,
  • Ram Chandra Barik,
  • Devendra Kumar Yadav,
  • Rajesh Kumar Sahoo

摘要

This paper presents a hybrid movie recommendation framework that integrates Deep Spiking Neural Networks (DSNN) with Collaborative Filtering (CF). CF is utilized to preprocess and complete sparse user–movie rating matrices, which are then used to train the DSNN for efficient and robust classification. The event-driven nature of DSNN enables lower computational cost while maintaining high predictive performance. After evaluating the MovieLens dataset, the proposed DSNN-CF model outperforms traditional machine learning and deep learning baselines in terms of precision, recall, and F1-score, achieving an average improvement of 10% in prediction accuracy for recommending movies of interest.