mlsanityData to 13 Oct 2026

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

arXiv 1809.07426 ↗First posted 19 Sep 2018cs.IR cs.LGcited by 475

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Abstract (arXiv)

Top-N sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-N ranked items that a user will likely interact in a `near future'. The order of interaction implies that sequential patterns play an important role where more recent items in a sequence have a larger impact on the next item. In this paper, we propose a Convolutional Sequence Embedding Recommendation Model (Caser) as a solution to address this requirement. The idea is to embed a sequence of recent items into an `image' in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters. This approach provides a unified and flexible network structure for capturing both general preferences and sequential patterns. The experiments on public datasets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics.

Key facts. Cited by 475 papers in the map (data through 13 Oct 2026). First posted on arXiv 19 Sep 2018.

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Citing papers per year: 2021: 60; 2022: 71; 2023: 75; 2024: 93; 2025: 95; 2026: 81.

475 papers.

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  1. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 397
  2. 2023Large Language Models are Zero-Shot Rankers for Recommender Systemscited by 308
  3. 2023Recommender Systems with Generative Retrievalcited by 192
  4. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  5. 2022Towards Universal Sequence Representation Learning for Recommender Systemscited by 120
  6. 2021Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyondcited by 118
  7. 2022Learning Vector-Quantized Item Representation for Transferable Sequential Recommenderscited by 112
  8. 2021Dive into Deep Learningcited by 111
  9. 2021STAN: Spatio-Temporal Attention Network for Next Location Recommendationcited by 102
  10. 2023How to Index Item IDs for Recommendation Foundation Modelscited by 102
  11. 2022Filter-enhanced MLP is All You Need for Sequential Recommendationcited by 96
  12. 2023PALR: Personalization Aware LLMs for Recommendationcited by 96
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  1. 2023DiffuRec: A Diffusion Model for Sequential Recommendationcited by 91
  2. 2022Intent Contrastive Learning for Sequential Recommendationcited by 90
  3. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 76
  4. 2023Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusioncited by 72
  5. 2021Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformercited by 69
  6. 2024On Softmax Direct Preference Optimization for Recommendationcited by 64
  7. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  8. 2021Contrastive Self-supervised Sequential Recommendation with Robust Augmentationcited by 56
  9. 2023Zero-Shot Next-Item Recommendation using Large Pretrained Language Modelscited by 53
  10. 2024IDGenRec: LLM-RecSys Alignment with Textual ID Learningcited by 50
  11. 2024Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender Systemcited by 50
  12. 2025Think Before Recommend: Unleashing the Latent Reasoning Power for Sequential Recommendationcited by 50
  13. 2024LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendationcited by 47
  14. 2025Reinforced Latent Reasoning for LLM-based Recommendationcited by 46
  15. 2021End-to-End User Behavior Retrieval in Click-Through RatePrediction Modelcited by 45
  16. 2022Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendationcited by 42
  17. 2023E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendationcited by 39
  18. 2021Sparse-Interest Network for Sequential Recommendationcited by 37
  19. 2022Sequential Recommendation via Stochastic Self-Attentioncited by 37
  20. 2023Diffusion Augmentation for Sequential Recommendationcited by 37
  21. 2024Plug-in Diffusion Model for Sequential Recommendationcited by 37
  22. 2021Black-Box Attacks on Sequential Recommenders via Data-Free Model Extractioncited by 36
  23. 2023TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterestcited by 35
  24. 2025Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representationscited by 35
  25. 2021Dynamic Graph Collaborative Filteringcited by 33
  26. 2024Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendationcited by 33
  27. 2024Customizing Language Models with Instance-wise LoRA for Sequential Recommendationcited by 32
  28. 2021Federated Neural Collaborative Filteringcited by 30
  29. 2022Decoupled Side Information Fusion for Sequential Recommendationcited by 30
  30. 2023MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendationcited by 30
  31. 2023LLMRec: Benchmarking Large Language Models on Recommendation Taskcited by 30
  32. 2023Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?cited by 30
  33. 2023gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Samplingcited by 29
  34. 2021Adversarial and Contrastive Variational Autoencoder for Sequential Recommendationcited by 28
  35. 2021Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Networkcited by 28
  36. 2023An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attentioncited by 28
  37. 2023A Survey on User Behavior Modeling in Recommender Systemscited by 26
  38. 2024Can Small Language Models be Good Reasoners for Sequential Recommendation?cited by 26

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