mlsanityData to 7 Oct 2026

From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews

arXiv 1303.4402 ↗First posted 18 Mar 2013cs.SI cs.IR physics.soc-phcited by 107

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Abstract (arXiv)

Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action movies; rather, we might conclude that they will eventually enjoy it -- once they are ready. The same is true for beers, wines, gourmet foods -- or any products where users have acquired tastes: the `best' products may not be the most `accessible'. Thus our goal in this paper is to recommend products that a user will enjoy now, while acknowledging that their tastes may have changed over time, and may change again in the future. We model how tastes change due to the very act of consuming more products -- in other words, as users become more experienced. We develop a latent factor recommendation system that explicitly accounts for each user's level of experience. We find that such a model not only leads to better recommendations, but also allows us to study the role of user experience and expertise on a novel dataset of fifteen million beer, wine, food, and movie reviews.

Key facts. Cited by 107 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Mar 2013.

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Citing papers per year: 2021: 4; 2022: 16; 2023: 15; 2024: 26; 2025: 22; 2026: 24.

107 papers.

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  11. 2022Square One Bias in NLP: Towards a Multi-Dimensional Exploration of the Research Manifoldcited by 29
  12. 2024Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representationcited by 25
38 more
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  11. 2025A Survey of Graph Transformers: Architectures, Theories and Applicationscited by 12
  12. 2025Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Timecited by 12
  13. 2022The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detectioncited by 11
  14. 2024SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detectioncited by 11
  15. 2024Confidence Calibration of Classifiers with Many Classescited by 11
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  17. 2024Weakly Supervised Anomaly Detection via Knowledge-Data Alignmentcited by 10
  18. 2025Can LLMs Find Fraudsters? Multi-level LLM Enhanced Graph Fraud Detectioncited by 10
  19. 2025FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detectioncited by 10
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  21. 2026From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detectioncited by 8
  22. 2024CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networkscited by 7
  23. 2024Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detectioncited by 7
  24. 2022UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendationcited by 6
  25. 2025Cooperation of Experts: Fusing Heterogeneous Information with Large Margincited by 6
  26. 2022Improving Fraud Detection via Hierarchical Attention-based Graph Neural Networkcited by 5
  27. 2023Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendationcited by 5
  28. 2023Dynamic Relation-Attentive Graph Neural Networks for Fraud Detectioncited by 5
  29. 2022Cyclical Curriculum Learningcited by 4
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  33. 2025Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detectioncited by 4
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  37. 2024BEADs: Bias Evaluation Across Domainscited by 3
  38. 2024Disentangling Likes and Dislikes in Personalized Generative Explainable Recommendationcited by 3

50 most cited of 107 All 107 (JSON)