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<p>Traditional sequential recommendation models often rely solely on user interaction histories, neglecting rich contextual signals like visual content and ratings. This narrow focus limits their ability to capture the full complexity of user preferences and fails to account for behavioral uncertainty. To address these challenges, we propose AVMSR, an Attentional Variational Hybrid CNN-LSTM model for sequential recommendation. AVMSR integrates multi-modal data by extracting visual features using CNNs and encoding user ratings through dense embeddings. These inputs are fused at each time step and processed by an LSTM to model temporal dynamics. An attention mechanism highlights influential historical interactions, while a variational inference component captures latent uncertainties via the reparameterization trick and KL divergence. This design enables a more expressive and robust user preference modeling. Experimental results on real-world datasets (MovieLens, Amazon, and Yelp) demonstrate that AVMSR significantly outperforms state-of-the-art approaches on Recall, NDCG, and MRR, validating the impact of multi-modal fusion and probabilistic reasoning.</p>

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An attentional variational multimodal CNN-LSTM model for sequential recommendations

  • 期刊论文

  • 发表日期：

    2026年8月5日

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  • Armielle Noulapeu Ngaffo,
  • Inès Raïssa Djouela Kamgang,
  • Ebenezer Maka Maka,
  • Yannick Malong

摘要

Traditional sequential recommendation models often rely solely on user interaction histories, neglecting rich contextual signals like visual content and ratings. This narrow focus limits their ability to capture the full complexity of user preferences and fails to account for behavioral uncertainty. To address these challenges, we propose AVMSR, an Attentional Variational Hybrid CNN-LSTM model for sequential recommendation. AVMSR integrates multi-modal data by extracting visual features using CNNs and encoding user ratings through dense embeddings. These inputs are fused at each time step and processed by an LSTM to model temporal dynamics. An attention mechanism highlights influential historical interactions, while a variational inference component captures latent uncertainties via the reparameterization trick and KL divergence. This design enables a more expressive and robust user preference modeling. Experimental results on real-world datasets (MovieLens, Amazon, and Yelp) demonstrate that AVMSR significantly outperforms state-of-the-art approaches on Recall, NDCG, and MRR, validating the impact of multi-modal fusion and probabilistic reasoning.