原文链接点这！
下面提供的论文，可以说基本都是经典中的经典。读完这些论文，相信对推荐系统的认识肯定会有质的飞越：（不够再找我。O(∩_∩)O~）
综述类:
1、Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions。最经典的推荐算法综述
2、Collaborative Filtering Recommender Systems. JB Schafer 关于协同过滤最经典的综述
3、Hybrid Recommender Systems: Survey and Experiments
4、项亮的博士论文《动态推荐系统关键技术研究》
5、个性化推荐系统的研究进展.周涛等
6、Recommender systems L Lü, M Medo, CH Yeung, YC Zhang, ZK Zhang, T Zhou Physics Reports 519 (1), 1-49 （https://arxiv.org/abs/1202.1112）
协同过滤：
1、matrix factorization techniques for recommender systems. Y Koren
2、Using collaborative filtering to weave an information Tapestry. David Goldberg （协同过滤第一次被提出）
3、Item-Based Collaborative Filtering Recommendation Algorithms. Badrul Sarwar , George Karypis, Joseph Konstan .etl
4、Application of Dimensionality Reduction in Recommender System – A Case Study. Badrul M. Sarwar, George Karypis, Joseph A. Konstan etl
5、Probabilistic Memory-Based Collaborative Filtering. Kai Yu, Anton Schwaighofer, Volker Tresp, Xiaowei Xu,and Hans-Peter Kriegel
6、Recommendation systems:a probabilistic analysis. Ravi Kumar Prabhakar Raghavan.etl
7、Amazon.com recommendations: item-to-item collaborative filtering. Greg Linden, Brent Smith, and Jeremy York
8、Evaluation of Item-Based Top- N Recommendation Algorithms. George Karypis
9、Probabilistic Matrix Factorization. Ruslan Salakhutdinov
10、Tensor Decompositions,Alternating Least Squares and other Tales. Pierre Comon, Xavier Luciani, André De Almeida
基于内容的推荐：
1、Content-Based Recommendation Systems. Michael J. Pazzani and Daniel Billsus
基于标签的推荐：
1、Tag-Aware Recommender Systems: A State-of-the-Art Survey. Zi-Ke Zhang(张子柯), Tao Zhou(周 涛), and Yi-Cheng Zhang(张翼成)
推荐评估指标：
1、推荐系统评价指标综述. 朱郁筱，吕琳媛
2、Accurate is not always good：How Accuacy Metrics have hurt Recommender Systems
3、Evaluating Recommendation Systems. Guy Shani and Asela Gunawardana
4、Evaluating Collaborative Filtering Recommender Systems. JL Herlocker
推荐多样性和新颖性：
1、Improving recommendation lists through topic diversification. Cai-Nicolas ZieglerSean M. McNee, Joseph A.Konstan,Georg Lausen
2、Fusion-based Recommender System for Improving Serendipity
3、Maximizing Aggregate Recommendation Diversity：A Graph-Theoretic Approach
4、The Oblivion Problem：Exploiting forgotten items to improve Recommendation diversity
5、A Framework for Recommending Collections
6、Improving Recommendation Diversity. Keith Bradley and Barry Smyth
推荐系统中的隐私性保护：
1、Collaborative Filtering with Privacy. John Canny
2、Do You Trust Your Recommendations? An Exploration Of Security and Privacy Issues in Recommender Systems. Shyong K “Tony” Lam, Dan Frankowski, and John Ried.
3、Privacy-Enhanced Personalization. Alfred Kobsa.etl
4、Differentially Private Recommender Systems：Building Privacy into the Netflix Prize Contenders. Frank McSherry and Ilya Mironov Microsoft Research,Silicon Valley Campus
5、When being Weak is Brave: Privacy Issues in Recommender Systems. Naren Ramakrishnan, Benjamin J. Keller,and Batul J. Mirza
推荐冷启动问题：
1、Tied Boltzmann Machines for Cold Start Recommendations. Asela Gunawardana.etl
2、Pairwise Preference Regression for Cold-start Recommendation. Seung-Taek Park, Wei Chu
3、Addressing Cold-Start Problem in Recommendation Systems. Xuan Nhat Lam.etl
4、Methods and Metrics for Cold-Start Recommendations. Andrew I. Schein, Alexandrin P opescul, Lyle H. U ngar
bandit(老虎机算法,可缓解冷启动问题):
1、Bandits and Recommender Systems. Jeremie Mary, Romaric Gaudel, Philippe Preux
2、Multi-Armed Bandit Algorithms and Empirical Evaluation
基于社交网络的推荐：
1.、Social Recommender Systems. Ido Guy and David Carmel
2、A Social Networ k-Based Recommender System(SNRS). Jianming He and Wesley W. Chu
3、Measurement and Analysis of Online Social Networks.
4、Referral Web：combining social networks and collaborative filtering
基于知识的推荐：
1、Knowledge-based recommender systems. Robin Burke
2、Case-Based Recommendation. Barry Smyth
3、Constraint-based Recommender Systems: Technologies and Research Issues. A. Felfernig. R. Burke
来源：CSDN
作者：追枫萨
链接：https://blog.csdn.net/m0_38052384/article/details/103842369