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？
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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