mlsanityData to 14 Oct 2026

Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns

arXiv 2312.16199 ↗First posted 23 Dec 2023cs.IR cs.LGcited by 11

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Abstract (arXiv)

The goal of session-based recommendation in E-commerce is to predict the next item that an anonymous user will purchase based on the browsing and purchase history. However, constructing global or local transition graphs to supplement session data can lead to noisy correlations and user intent vanishing. In this work, we propose the Frequent Attribute Pattern Augmented Transformer (FAPAT) that characterizes user intents by building attribute transition graphs and matching attribute patterns. Specifically, the frequent and compact attribute patterns are served as memory to augment session representations, followed by a gate and a transformer block to fuse the whole session information. Through extensive experiments on two public benchmarks and 100 million industrial data in three domains, we demonstrate that FAPAT consistently outperforms state-of-the-art methods by an average of 4.5% across various evaluation metrics (Hits, NDCG, MRR). Besides evaluating the next-item prediction, we estimate the models' capabilities to capture user intents via predicting items' attributes and period-item recommendations.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 33 of its 51 references that are in the map. First posted on arXiv 23 Dec 2023.

Builds on

33 of 51 references.

References · 33

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21 more
  1. 2021GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graphcited by 143
  2. 2022Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learningcited by 86
  3. 2021Global Context Enhanced Graph Neural Networks for Session-based Recommendationcited by 81
  4. 2020Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendationcited by 75
  5. 2019Rethinking the Item Order in Session-based Recommendation with Graph Neural Networkscited by 43
  6. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
  7. 2021Interest-aware Message-Passing GCN for Recommendationcited by 39
  8. 2022Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Networkcited by 38
  9. 2022CORE: Simple and Effective Session-based Recommendation within Consistent Representation Spacecited by 37
  10. 2019Neural Subgraph Isomorphism Countingcited by 27
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  12. 2022Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphscited by 21
  13. 2021QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extractioncited by 13
  14. 2022Boosting Graph Structure Learning with Dummy Nodescited by 13
  15. 2021Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matchingcited by 11
  16. 2021Learning Multi-granularity User Intent Unit for Session-based Recommendationcited by 11
  17. 2022Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunitiescited by 9
  18. 2022M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendationscited by 9
  19. 2022Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendationcited by 8
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  21. 2023CCGen: Explainable Complementary Concept Generation in E-Commercecited by 5

Built on by

Cited by year

Citing papers per year: 2024: 5; 2025: 6.

11 papers.

  1. 2024Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Modelscited by 20
  2. 2024MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understandingcited by 10
  3. 2024Intention Knowledge Graph Construction for User Intention Relation Modelingcited by 6
  4. 2025SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understandingcited by 6
  5. 2025Exploiting Inter-Session Information with Frequency-enhanced Dual-Path Networks for Sequential Recommendationcited by 2
  6. 2024Forecasting Live Chat Intent from Browsing Historycited by 1
  7. 2025Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendationcited by 1
  8. 2024Designing an Interpretable Interface for Contextual Banditscited by 0
  9. 2025An item is worth one token in Multimodal Large Language Models-based Sequential Recommendationcited by 0
  10. 2025Potent but Stealthy: Rethink Profile Pollution against Sequential Recommendation via Bi-level Constrained Reinforcement Paradigmcited by 0
  11. 2025LLM-Empowered Representation Learning for Emerging Item Recommendationcited by 0