Some links of recommender system that I collected once for my incoming freshmen.

1. 探索推荐引擎内部的秘密
http://www.ibm.com/developerworks/cn/web/1103_zhaoct_recommstudy1/index.html
购物网站的推荐算法有哪些？
http://www.zhihu.com/question/19967564
http://www.zhihu.com/question/19558085
基于矩阵分解的推荐算法
http://www.cnblogs.com/kobedeshow/p/3651833.html?utm_source=tuicool&utm_medium=referral
其关于推荐的几篇博文 http://www.cnblogs.com/kobedeshow/category/553408.html
推荐系统经典论文文献及业界应用
http://blog.csdn.net/dustinsea/article/details/17529075
一个人搜集的博客集合
http://blog.csdn.net/zyvscc/article/category/1124766
推荐系统(recommender system)经典论文文献及业界应用
http://www.insidecomputing.org/?p=168
推荐系统的坑
http://www.36dsj.com/archives/21360
采用深度学习算法为Spotify做基于内容的音乐推荐
http://www.36dsj.com/archives/23959
推荐系统
http://www.36dsj.com/?s=%E6%8E%A8%E8%8D%90%E7%B3%BB%E7%BB%9F
推荐系统与推荐方法
http://blog.sciencenet.cn/blog-1191257-839745.html
个性化算法如何笑傲双11
http://www.csdn.net/article/2015-11-16/2826215
New Directions in Recommender Systems —— 商品推荐算法 & 推荐解释
http://www.douban.com/note/484692347/
投资组合策略在推荐系统中的应用
http://www.douban.com/note/345682756/
豆瓣的个性化推荐
http://www.douban.com/note/242318013/
中欧商学院演讲：Netflix的竞争策略
http://www.douban.com/note/341214710/
推荐引擎反思
http://www.douban.com/note/245740667/
每周论文总结
http://www.douban.com/note/260211750/
推荐引擎
http://www.douban.com/note/515888223/
个性化推荐系统综述
http://www.douban.com/note/169915691/
推荐算法综述笔记
http://www.douban.com/note/504065603/
语义分析的一些方法
http://www.flickering.cn/ads/2015/02/%E8%AF%AD%E4%B9%89%E5%88%86%E6%9E%90%E7%9A%84%E4%B8%80%E4%BA%9B%E6%96%B9%E6%B3%95%E4%B8%80/
http://www.flickering.cn/ads/2015/02/%E8%AF%AD%E4%B9%89%E5%88%86%E6%9E%90%E7%9A%84%E4%B8%80%E4%BA%9B%E6%96%B9%E6%B3%95%E4%BA%8C/
http://blog.sciencenet.cn/blog-1225851-889651.html

2. Recommendation and Ratings Public Data Sets For Machine Learning
https://gist.github.com/entaroadun/1653794
Datasets
http://kevinchai.net/
Entree Chicago Recommendation Data Data Set
https://archive.ics.uci.edu/ml/datasets/Entree+Chicago+Recommendation+Data
推荐系统公共资源汇总
http://aoyouzi.iteye.com/blog/1845235
GroupLens Research
http://grouplens.org/datasets/
Evaluating Recommendation Systems
http://research.microsoft.com/pubs/115396/evaluationmetrics.tr.pdf
Improving Recommendation Lists Through Topic Diversification
http://files.grouplens.org/papers/ziegler-www05.pdf
Sequential event prediction
http://web.mit.edu/rudin/www/LethamRuMa13.pdf
Factorization meets the neighborhood: a multifaceted collaborative filtering model
http://dl.acm.org/citation.cfm?id=1401944
《ORec: An Opinion-Based Point-of-Interest Recommendation Framework》（郑宇）
http://www.cs.cityu.edu.hk/~jzhang26/paper/ORec.pdf
《Recommendations in location-based social networks: a survey》（郑宇）
https://www.researchgate.net/profile/Jie_Bao9/publication/272389361_Recommendations_in_location-
based_social_networks_a_survey/links/55261c570cf25d66dc947c48.pdf
《Inferring air quality for station location recommendation based on urban big data》（郑宇）
http://140.112.31.186/~mslab/publications/Conference/2014/Inferring%20Air.pdf
Collaborative location and activity recommendations （郑宇）
http://dl.acm.org/citation.cfm?id=1772795
case study and application of recommender system
https://www.researchgate.net/post/Does_anyone_have_any_case_study_or_research_paper_on_the_applications_of_recommendation_systems_other_than_schools
Recommendation systems: Principles, methods and evaluation
http://www.sciencedirect.com/science/article/pii/S1110866515000341
A Social Formalism and Survey for Recommender Systems
http://dl.acm.org/citation.cfm?id=2783705
Recommender System Application Developments: A Survey
http://www.uts.edu.au/sites/default/files/desi-publication-recommender%20system%20application%20developments%20a%20survey-accepted%20menuscript.pdf
Comparative Study on Approaches of Recommendation System
http://research.ijcaonline.org/volume118/number2/pxc3903059.pdf
A Survey of Point-of-Interest Recommendation in Location-Based Social Networks
Million Song Dataset Challenge
https://www.kaggle.com/c/msdchallenge

3. https://www.aaai.org/ocs/index.php/WS/AAAIW15/paper/viewFile/10132/10253
Recommendations in Location-based Social Networks: A Survey
http://research.microsoft.com/pubs/191797/LBSN-survey.pdf
libFM: Factorization Machine Library（台湾大学林智仁libSVM团队写的）
http://libfm.org/
A Simple Tutorial on the LibRec
http://www.librec.net/tutorial.html
Online Courses Recommendation based on LDA
http://ceur-ws.org/Vol-1318/paper5.pdf
Using Topic Models in Content-Based News Recommender Systems
http://stp.lingfil.uu.se/nodalida/2013/pdf/NODALIDA22.pdf
Main content areaA Probabilistic Recommendation Method Inspired by Latent Dirichlet Allocation Model
http://search.proquest.com/openview/ae33797692ec61d2ba17e8bbe392b39b/1?pq-origsite=gscholar
LDA-based Personalized Document Recommendation
http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1012&context=pacis2013
Comparing Topic Models for a Movie Recommendation System(作者很牛，但查不到改文章所在刊物)
https://www.dbgroup.unimo.it/~po/pubs/WEBIST_2014.pdf
Topic Modeling for Personalized Recommendation of Volatile Items http://www.lix.polytechnique.fr/~maks/papers/recommend.pdf
Latent Dirichlet Allocation for Tag Recommendation (RecSys 2009)
https://www.researchgate.net/profile/Ralf_Krestel/publication/221141032_Latent_dirichlet_allocation_for_tag_recommendation/links/02e7e51e42a994f415000000.pdf
Collaborative Topic Modeling for Recommending Scientific Articles
https://www.cs.princeton.edu/~chongw/papers/WangBlei2011.pdf
Auralist: Introducing Serendipity into Music Recommendation
http://www.cl.cam.ac.uk/~dq209/publications/zhang12auralist.pdf
A recommendation system combining LDA and collaborative filtering method for Scenic Spot
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7120564
SCENE : A Scalable Two-Stage Personalized News Recommendation System (SIGIR11, A会)
http://users.cis.fiu.edu/~taoli/pub/p125-li-sigir2011.pdf
Deep Learning of Semantic Word Representations to Implement a Content-based Recommender for the RecSys Challenge’14（深度学习的概念）
http://2014.eswc-conferences.org/sites/default/files/eswc2014-challenges_rs_submission_27.pdf
Improving Music Recommendation in Session-Based Collaborative Filtering by using Temporal Context（时间上下文）
http://www.di.fc.ul.pt/~mjf/publications/2014-2010/pdf/ictai13.pdf
Ranking in Context-Aware Recommender Systems（B会的poster WWW2011）
http://www.ambuehler.ethz.ch/CDstore/www2011/companion/p65.pdf
Modeling Location-based User Rating Profiles for Personalized Recommendation（TKDD2014，有LDA的公式）
http://net.pku.edu.cn/~cuibin/Papers/2014-tkdd.pdf
MovieRecommend 源代码 （应该是基于描述内容的）
https://github.com/abhowmick22/MovieRecommend
fLDA: Matrix Factorization through Latent Dirichlet Allocation （B会，WSDM）
http://www.wsdm-conference.org/2010/proceedings/docs/p91.pdf
online LDA
On-Line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking （2008，ICDM，B会）
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4781095
Online Variational Inference for the Hierarchical Dirichlet Process（引用率，140）

4. http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2011_WangPB11.pdf
Online Learning for Latent Dirichlet Allocation （NIPS， B会）
http://people.ee.duke.edu/~lcarin/HoffmanBleiBach2010b.pdf
Soft-constraint based online LDA for community recommendation (PCM, CCF C会)
http://link.springer.com/chapter/10.1007%2F978-3-642-15696-0_46?LI=true#page-1
在某一主题上，选取最近的几个作为其在该主题上的朋友（假设不好，因为忽略了主题领袖的影响效果）。也就是说，朋友是主题意义上的朋友。（《Leveraging tagging for neighborhood-aware probabilistic
matrix factorization》把距离上最近的作为朋友）
可以根据其Jaccard similarity coefficien来判断是否是朋友
影响是在主题上的影响。
结合矩阵分解，影响是在用户向量上。
同一用户在不同主题上的影响力，应该用该用户的影响力（social trust）加权。