最好走的路越走越难,最难走的路越走越容易

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Tag Archives: favor

个性化技术相关资料

经常会有朋友发 email 问我,“研究个性化技术应该如何入手”?
这个问题其实挺让我为难的,因为以我目前的水平,尚不足以授人以渔。但又不好不作答,因此,整理了这份资料清单。我争取长期维护下去,力求授人以鱼吧。
注:此清单完全以我个人喜好整理,不周之处还请大家在评论里指明,或者 email/gtalk 也可。

Subjects:

  1. Recommender System
  2. Collaborative Filtering
  3. Long Tail

Groups & Researchers:

  1. GroupLens Lab
  2. Karypis Lab
  3. Thomas Hofmann
  4. David M. Pennock

Conferences:

  1. RecSys 2007
  2. KDD Cup and Workshop 2007

Selected Papers:

  1. Paul Resnick, GroupLens — An Open Architecture for Collaborative Filtering of Netnews
  2. Badrul Sarwar, Item-based Collaborative Filtering Recommendation Algorithms
  3. Greg Linden, Amazon.com Recommendations: Item-to-Item Collaborative Filtering
  4. Thomas Hofmann, Latent Semantic Models for Collaborative Filtering
  5. 4 googlers, Google News Personalization – Scalable Online Collaborative Filtering

Blogers:

  1. Greg Linden
  2. Daniel Lemire
  3. Duke Listens!

Software Libraries:

  1. Taste, Java, http://taste.sf.net/
  2. Beyond Thoth, C#, http://sf.net/projects/beyondthoth/

Resources:

  1. Stanford CS345: Data Mining
  2. http://del.icio.us/tag/collaborativefiltering
  3. http://del.icio.us/tag/recommendersystem

Books:
完全以个性化技术为中心的书籍很少,但多数讲 Machine Learning 或者 Data Mining 的书籍里面,都会有专门的章节,介绍与此相关的内容。

  1. Programming Collective Intelligence, Toby Segaran, O'Reilly, 2007.8
  2. Personalization Techniques and Recommender Systems, Gulden Uchyigit, World Scientific, 2008.4
 

ResysChina 发起人
1. 持续关注 个性化推荐 技术;
2. 持续关注 Semantic Web 技术;
3. 评论与上两项相关的互联网业务与产品;

我相信技术的力量!
wendell.gu@GMail.com

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