mlsanityData to 14 Oct 2026

Attention Mixtures for Time-Aware Sequential Recommendation

arXiv 2304.08158 ↗First posted 17 Apr 2023cs.IR cs.LGcited by 13

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Abstract (arXiv)

Transformers emerged as powerful methods for sequential recommendation. However, existing architectures often overlook the complex dependencies between user preferences and the temporal context. In this short paper, we introduce MOJITO, an improved Transformer sequential recommender system that addresses this limitation. MOJITO leverages Gaussian mixtures of attention-based temporal context and item embedding representations for sequential modeling. Such an approach permits to accurately predict which items should be recommended next to users depending on past actions and the temporal context. We demonstrate the relevance of our approach, by empirically outperforming existing Transformers for sequential recommendation on several real-world datasets.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 33 of its 60 references that are in the map. First posted on arXiv 17 Apr 2023.

Builds on

33 of 60 references.

References · 33

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018Self-Attentive Sequential Recommendationcited by 1,184
  3. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  4. 2019Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Prunedcited by 856
  5. 2019Are Sixteen Heads Really Better than One?cited by 816
  6. 2015Image-based Recommendations on Styles and Substitutescited by 706
  7. 2018Session-based Recommendation with Graph Neural Networkscited by 312
  8. 2019A Survey on Session-based Recommender Systemscited by 97
  9. 2018Sequence-Aware Recommender Systemscited by 58
  10. 2019Self-attention with Functional Time Representation Learningcited by 52
  11. 2018RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendationcited by 51
  12. 2017ATRank: An Attention-Based User Behavior Modeling Framework for Recommendationcited by 45
21 more
  1. 2019Rethinking the Item Order in Session-based Recommendation with Graph Neural Networkscited by 43
  2. 2017Current Challenges and Visions in Music Recommender Systems Researchcited by 42
  3. 2022Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendationcited by 42
  4. 2017Latent Relational Metric Learning via Memory-based Attention for Collaborative Rankingcited by 39
  5. 2019Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systemscited by 28
  6. 2020Modeling Personalized Item Frequency Information for Next-basket Recommendationcited by 24
  7. 2022CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attentioncited by 22
  8. 2022Multi-Behavior Sequential Recommendation with Temporal Graph Transformercited by 20
  9. 2022Enhancing Sequential Recommendation with Graph Contrastive Learningcited by 18
  10. 2020MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential Recommendationcited by 17
  11. 2019Collaborative Translational Metric Learningcited by 16
  12. 2022Effective and Efficient Training for Sequential Recommendation using Recency Samplingcited by 13
  13. 2022Flow Moods: Recommending Music by Moods on Deezercited by 11
  14. 2022Contrastive Learning with Bidirectional Transformers for Sequential Recommendationcited by 10
  15. 2022FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddingscited by 9
  16. 2022Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendationcited by 8
  17. 2021Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendationcited by 7
  18. 2021Eigen Analysis of Self-Attention and its Reconstruction from Partial Computationcited by 6
  19. 2021Hierarchical Latent Relation Modeling for Collaborative Metric Learningcited by 6
  20. 2022Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendationcited by 6
  21. 2022UFNRec: Utilizing False Negative Samples for Sequential Recommendationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 5.

13 papers.

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  1. 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representationscited by 35
  2. 2025Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendationcited by 15
  3. 2024Retrieval of Temporal Event Sequences from Textual Descriptionscited by 6
  4. 2024UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential Recommendationscited by 4
  5. 2024Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendationcited by 4
  6. 2025Balancing Accuracy and Novelty with Sub-Item Popularitycited by 1
  7. 2025When and What to Recommend: Joint Modeling of Timing and Content for Active Sequential Recommendationcited by 1
  8. 2026T-RoPE: Time-Aware Rotary Position Embedding for Sequential Recommendationcited by 1
  9. 2024Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attentioncited by 0
  10. 2026TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendationcited by 0
  11. 2026Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contextscited by 0
  12. 2026Uniboost: Global Coordination with Value Alignment for Fair and Efficient Traffic Allocationcited by 0
1 more
  1. 2026The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenderscited by 0