推荐系统经典论文

转：https://blog.csdn.net/Allenalex/article/details/79491483

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）

个性化推荐系统评价方法综述.周涛等
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 Ziegler

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

Fusion-based Recommender System for Improving Serendipity

Maximizing Aggregate Recommendation Diversity：A Graph-Theoretic Approach

The Oblivion Problem：Exploiting forgotten items to improve Recommendation diversity

A Framework for Recommending Collections

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

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

Measurement and Analysis of Online Social Networks.

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

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

关于找论文的一些建议： 
看影响因子；看发表的期刊和会议；看论文作者以及作者的机构。 
经典论文的引用一般也都不错。

下次整理下高质量推荐系统论文常发表的期刊和会议。以及有哪些比赛，有哪些大神。
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作者：aturbofly 
来源：CSDN 
原文：https://blog.csdn.net/Allenalex/article/details/79491483 
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