【推荐系统】那些年, 引用量超1000的经典推荐系统论文

点击上方，选择星标或置顶，每天给你送干货！

阅读大概需要9分钟

跟随小博主，每天进步一丢丢

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来自 | 知乎 

作者 | 育心

链接 | https://zhuanlan.zhihu.com/p/183753290

编辑 | 机器学习与推荐算法

之前按照时间线的方式整理和梳理了56篇经典的推荐系统论文，详见一文尽览推荐系统模型演变史。值得注意的是，其中大部分论文的引用量超过了一千，有的甚至过了万。

今天，我们换个角度来梳理下这些年推荐系统领域超过一千引用量的论文。回想一下你阅读了其中的多少篇呢，你的论文贡献了多少引用量给这些经典论文呢？下一篇的经典论文又会花落谁家呢？

注：加粗数字表示引用量，引用量按照由少到多排序

1. 1012-Ontological user profiling in recommender systems.

2. 1031-Deep Neural Networks for YouTube Recommendations.

3. 1060-Internet Recommendation Systems.

4. 1072-Trust in recommender systems.

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

6. 1088- Collaborative Filtering Recommender Systems.

7. 1100-Advances in Collaborative Filtering.

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

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

10. 1219-Trust-aware recommender systems.

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

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

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

14. 1344-Evaluating Recommendation Systems.

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

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

17. 1417-Neural Collaborative Filtering.

18. 1420-Recommender systems with social regularization.

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

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

21. 1502-Hybrid web recommender systems.

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

23. 1672-The Netflix Prize.

24. 1753-Latent semantic models for collaborative filtering.

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

26. 1797-Improving recommendation lists through topic diversification.

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

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

29. 1835-Propagation of trust and distrust.

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

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

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

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

34. 2037-Collaborative filtering recommender systems.

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

36. 2362-Recommender systems survey.

37. 2364-Context-Aware Recommender Systems.

38. 2431-E-Commerce Recommendation Applications.

39. 2449-Collaborative Filtering for Implicit Feedback Datasets.

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

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

42. 2779-Content-based recommendation systems.

43. 2897-Collaborative filtering with temporal dynamics.

44. 2980-The dynamics of viral marketing.

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

46. 3618-A Survey of Collaborative Filtering Techniques.

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

48. 6520-Evaluating collaborative filtering recommender systems.

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

50. 7459-Matrix Factorization Techniques for Recommender Systems.

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

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

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✈ https://github.com/hongleizhang/RSPapers

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