下面提供的論文，可以說基本都是經典中的經典。讀完這些論文，相信對推薦系統的認識肯定會有質的飛越：（不夠再找我。O(∩_∩)O~）

綜述類:

1、Towards the
Next Generation of Recommender Systems: A Survey of the State-of-the-Art and
Possible Extensions。最經典的推薦演算法綜述

2、Collaborative Filtering Recommender Systems. JB Schafer 關於協同過濾最經典的綜述

3、Hybrid Recommender Systems: Survey and Experiments

4、項亮的博士論文《動態推薦系統關鍵技術研究》

5、個性化推薦系統的研究進展.周濤等

6、Recommender systems L Lü, M Medo, CH Yeung, YC Zhang, ZK Zhang, T Zhou

1. 個性化推薦系統評價方法綜述.周濤等

協同過濾：

1.matrix factorization techniques for recommender systems. Y Koren

2.Using collaborative filtering to weave an information Tapestry. David Goldberg （協同過濾第一次被提出）

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

基於內容的推薦：
1.Content-Based Recommendation Systems. Michael J. Pazzani and Daniel Billsus

基於標籤的推薦：
1.Tag-Aware Recommender Systems: A State-of-the-Art Survey. Zi-Ke Zhang(張子柯), Tao Zhou(周 濤), and Yi-Cheng Zhang(張翼成)

推薦評估指標：
1、推薦系統評價指標綜述. 朱鬱筱，呂琳媛

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

推薦多樣性和新穎性：
1. Improving recommendation lists through topic diversification. Cai-Nicolas Ziegler

Sean M. McNee, Joseph A.Konstan,Georg Lausen

1. Fusion-based Recommender System for Improving Serendipity

2. Maximizing Aggregate Recommendation Diversity：A Graph-Theoretic Approach

3. The Oblivion Problem：Exploiting forgotten items to improve Recommendation diversity

4. A Framework for Recommending Collections

5. Improving Recommendation Diversity. Keith Bradley and Barry Smyth

推薦系統中的隱私性保護：
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

推薦冷啟動問題：
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(老虎機演算法,可緩解冷啟動問題):
1、Bandits and Recommender Systems. Jeremie Mary, Romaric Gaudel, Philippe Preux

2、Multi-Armed Bandit Algorithms and Empirical Evaluation

基於社交網路的推薦：
1. Social Recommender Systems. Ido Guy and David Carmel

1. A Social Networ k-Based Recommender System(SNRS). Jianming He and Wesley W. Chu

2. Measurement and Analysis of Online Social Networks.

3. Referral Web：combining social networks and collaborative filtering

基於知識的推薦：
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

其他：
Trust-aware Recommender Systems. Paolo Massa and Paolo Avesani

關於找論文的一些建議：
看影響因子；看發表的期刊和會議；看論文作者以及作者的機構。
經典論文的引用一般也都不錯。

下次整理下高質量推薦系統論文常發表的期刊和會議。以及有哪些比賽，有哪些大神。