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<p>Sequential recommendation has achieved remarkable progress with self-attentive architectures such as SASRec (Self-Attentive Sequential Recommendation), yet existing approaches often underutilize semantic relationships among items and fail to model temporal dynamics effectively. These limitations reduce the ability of existing systems to capture nuanced user preferences in real-world settings. To address these issues, this work introduces two novel frameworks: one for effective semantic integration and another for a semantic and temporal-aware hybrid embedding generation that enhances the representational capacity of SASRec. These frameworks construct a semantically rich hybrid matrix utilizing <b>M</b>arkov State Transition probabilities, <b>C</b>osine similarity, <b>P</b>ersonalized PageRank(PPR) based normalization and additionally <b>T</b>ime-weighted decay information for the temporal variant. The constructed hybrid matrix is smoothed using a graph convolutional network (GCN) to generate item embeddings, which are then passed to the transformer-based sequential recommendation model SASRec for next-item prediction. Evaluated on three real-world benchmark datasets (MovieLens, Yelp and Amazon Beauty), our proposed temporal variant achieves up to 10.4% improvement in HR@10 and 8.2% in NDCG@10 over SASRec, while maintaining competitive efficiency. Our studies confirm the effectiveness of each component, including semantic graph construction, temporal weighting, and contrastive alignment. These results demonstrate that incorporating semantic and temporal signals into sequential recommenders substantially enhances both accuracy and relevancy of recommendations.</p>

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Semantic and temporal-aware hybrid embedding for transformer-based sequential recommendation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月11日

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  • Smriti Vipin Madangarli,
  • G. Suganeshwari,
  • S. P. Syed Ibrahim,
  • Muthu Subash Kavitha,
  • Norio Setozaki

摘要

Sequential recommendation has achieved remarkable progress with self-attentive architectures such as SASRec (Self-Attentive Sequential Recommendation), yet existing approaches often underutilize semantic relationships among items and fail to model temporal dynamics effectively. These limitations reduce the ability of existing systems to capture nuanced user preferences in real-world settings. To address these issues, this work introduces two novel frameworks: one for effective semantic integration and another for a semantic and temporal-aware hybrid embedding generation that enhances the representational capacity of SASRec. These frameworks construct a semantically rich hybrid matrix utilizing Markov State Transition probabilities, Cosine similarity, Personalized PageRank(PPR) based normalization and additionally Time-weighted decay information for the temporal variant. The constructed hybrid matrix is smoothed using a graph convolutional network (GCN) to generate item embeddings, which are then passed to the transformer-based sequential recommendation model SASRec for next-item prediction. Evaluated on three real-world benchmark datasets (MovieLens, Yelp and Amazon Beauty), our proposed temporal variant achieves up to 10.4% improvement in HR@10 and 8.2% in NDCG@10 over SASRec, while maintaining competitive efficiency. Our studies confirm the effectiveness of each component, including semantic graph construction, temporal weighting, and contrastive alignment. These results demonstrate that incorporating semantic and temporal signals into sequential recommenders substantially enhances both accuracy and relevancy of recommendations.