为《物理学报导》写了一篇很长的关于推荐系统的综述~~

可以通过链接 http://arxiv.org/abs/1202.1112  下载到全文，估计正式的版本4月份会印出来~~

1. 推荐系统说白了就是通过分析用户以前的行为轨迹（比如在网上买过哪些书，访问过哪些网页），主动向用户推荐他们可能感兴趣的内容~~

2.信息导航（被动）和信息推荐（主动）在这个信息爆炸的时代对于我们越来越重要，所以我觉得大家不妨关注这方面的研究~~

3.大家可能觉得奇怪，这是一个信息科学的问题，为什么我们会写物理期刊的综述~~因为我们投计算机方面的可能会被枪毙~~其实最近除了在两个还不错的会议（CIKM &. WSDM）上面写过推荐相关的论文，我们组的工作主要都是发表在物理类期刊 EPL, PRL, PRE,NJP,Physica A和综合类期刊PLoS ONE, PNAS上面，计算机方面的期刊文章很少~~但是我觉得物理和计算机在信息挖掘方面的交叉非常重要，是真正能够产生不错的研究成果的~~我们的工作在计算机领域的引用其实很不错，我们也引用很多计算机领域的文章，但是双方描述问题的风格和解决问题的手段还是有很多代沟~~

4.我们在这方面也作了一些实际的应用  www.baifendian.com

5.和推荐系统密切相关的一篇综述是 http://www.sciencedirect.com/science/article/pii/S037843711000991X  这个工作影响力也还不错，发表不到一年就有34个引用，我估计5年内能到三四百（因为第一年引用很少） 

挺好玩的方向，也是我最近几年3大主要研究方向之一，也希望各位大牛多多关注，多多批评指正！

题目：Recommender Systems

作者：Linyuan Lu, Matus Medo, Chi Ho Yeung, Yi-Cheng Zhang, Zi-Ke Zhang, Tao Zhou

摘要：The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.