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推荐系统 | 引用量超过1000的52篇经典论文

2020-08-14 12:00 作者:深蓝学院  | 我要投稿

注:数字表示引用量

  1. 1012-Ontological user profiling in recommender systems.pdf

  2. 1031-Deep Neural Networks for YouTube Recommendations.pdf

  3. 1060-Internet Recommendation Systems.pdf

  4. 1072-Trust in recommender systems.pdf

  5. 1086-Being accurate is not enough: how accuracy metrics have hurt recommender systems.pdf

  6. 1088- Collaborative Filtering Recommender Systems.pdf

  7. 1100-Advances in Collaborative Filtering.pdf

  8. 1111-Method, system and personalized web content manager responsive to browser viewers' psychological preferences, behavioral responses and physiological stress indicators.pdf

  9. 1204-Hidden factors and hidden topics:understanding rating dimensions with review text.pdf

  10. 1219-Trust-aware recommender systems.pdf

  11. 1233-A matrix factorization technique with trust propagation for recommendation in social networks.pdf

  12. 1253 - Performance of Recommender Algorithms on Top-N Recommendation Tasks.pdf

  13. 1282-SoRec:social recommendation using probabilistic matrix factorization.pdf

  14. 1344-Evaluating Recommendation Systems.pdf

  15. 1391-Taking the Human Out of the Loop:A Review of Bayesian Optimization.pdf

  16. 1395-Content-based Recommender Systems:State of the Art and Trends.pdf

  17. 1417-Neural Collaborative Filtering.pdf

  18. 1420-Recommender systems with social regularization.pdf

  19. 1461-Collaborative topic modeling for recommending scientific articles.pdf

  20. 1468-Incorporating contextual information in recommender systems using a multidimensional approach.pdf

  21. 1502-Hybrid web recommender systems.pdf

  22. 1586-A contextual-bandit approach to personalized news article recommendation.pdf

  23. 1672-The Netflix Prize.pdf

  24. 1753-Latent semantic models for collaborative filtering.pdf

  25. 1794-The MovieLens Datasets:History and Context.pdf

  26. 1797-Improving recommendation lists through topic diversification.pdf

  27. 1812-Content-boosted collaborative filtering for improved recommendations.pdf

  28. 1829-Content-based book recommending using learning for text categorization.pdf

  29. 1835-Propagation of trust and distrust.pdf

  30. 1867-Eigentaste:A Constant Time Collaborative Filtering Algorithm.pdf

  31. 1877-What makes a helpful online review?a study of customer reviews on amazon.com.pdf

  32. 1880-Application of Dimensionality Reduction in Recommender System - A Case Study.pdf

  33. 1899-The influence of online product recommendations on consumers' online choices.pdf

  34. 2037-Collaborative filtering recommender systems.pdf

  35. 2056-Methods and metrics for cold-start recommendations.pdf

  36. 2362-Recommender systems survey.pdf

  37. 2364-Context-Aware Recommender Systems.pdf

  38. 2431-E-Commerce Recommendation Applications.pdf

  39. 2449-Collaborative Filtering for Implicit Feedback Datasets.pdf

  40. 2557-Item-based top- N recommendation algorithms.pdf

  41. 2693-A distributed, architecture-centric approach to computing accurate recommendations from very large and sparse datasets.pdf

  42. 2779-Content-based recommendation systems.pdf

  43. 2897-Collaborative filtering with temporal dynamics.pdf

  44. 2980-The dynamics of viral marketing.pdf

  45. 3224-Factorization meets the neighborhood:a multifaceted collaborative filtering model.pdf

  46. 3618-A Survey of Collaborative Filtering Techniques.pdf

  47. 4656-Hybrid Recommender Systems:Survey and Experiments.pdf

  48. 6520-Evaluating collaborative filtering recommender systems.pdf

  49. 6948-Amazon.com recommendations:item-to-item collaborative filtering.pdf

  50. 7459-Matrix Factorization Techniques for Recommender Systems.pdf

  51. 9317-Item-based collaborative filtering recommendation algorithms.pdf

  52. 11644-Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions.pdf

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