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Roger Chen

Content analysis and the cold-start problem - Duke Listens! - 0 views

blogs.sun.com/...content_analysis_and_the_cold

recommender

shared by Roger Chen on 25 Apr 08 - Cached
  • A classic problem in traditional collaborative filtering recommendation is the 'cold start' problem. It is hard to generate recommendations for new items because there isn't enough taste data about the new items to make reliable correlations with other items. That's where content analysis comes in. The cold start problem can be alleviated by basing recommendations on similarity of content as well as the wisdom of the crowds. New items can be analyzed and enrolled into a recommender, making these items available and recommendable.
  • Roger Chen on 25 Apr 08
     
    The cold-start problem is common in tranditional CF system. Conten analysis comes in to prevent form suffering from cold-start.
Roger Chen

Lorcan Dempsey's weblog: Recommendation and Ranganathan - 0 views

orweblog.oclc.org/...001566.html

recommender

shared by Roger Chen on 15 May 08 - Cached
  • Now, typically library catalogs use traditional information retrieval techniques over professionally produced metadata. This is not a lot of data to play with! We have just begun to see interesting things being done with the other types of data as libraries explore the use of transactional data for recommendations and look to incorporate contributed data.
  • Google, Amazon and other sites license professionally produced metadata. But in different ways they also use the other types of data also.
  • Suggestion, or recommendation, is becoming increasingly a part of our everyday web experience,and improving the quality of suggestion has become an important goal for many services. Clearly, there are commercial interests riding on this.
  • ...2 more annotations...
  • "The 20th century was about sorting out supply," Potter says. "The 21st is going to be about sorting out demand." The Internet makes everything available, but mere availability is meaningless if the products remain unknown to potential buyers.
  • When I get good recommendations, I spend my time and money differently. Even better recommendations will dramatically increase the value of that time and money.
Roger Chen

Recommenders06 » What is a recommender system? - 0 views

blog.recommenders06.com/?p=31

recommender

shared by Roger Chen on 26 Dec 08 - Cached
  • Many types of recommender systems exist such as non-personalized, demographic, content based, content based, collaborative (user based), collaborative (item based) and model based. Item based collaborative models have been applied successfully in commercial settings thanks to scalability and quality advantages as compared to others. Model based approaches differ from the rest which rely on memory of events. Instead they involve the creation of a probabilistic, decision tree or neural net model that attempts to identify the underlying logic to users’ choices.
  • Sparsity, Scalability, Cold-start, Implicit Ratings, Dealing with Multiple Criteria, Context-dependant Recommendations.
Roger Chen

TOCProceedings of the 2007 ACM conference on Recommender systems - 0 views

portal.acm.org/toc.cfm

papers recommender

shared by Roger Chen on 01 Jul 08 - Cached
  • Roger Chen on 01 Jul 08
     
    Proceedings of the 2007 ACM conference on Recommender systems, 2007, Minneapolis, MN, USA October 19 - 20, 2007
Roger Chen

Collaborative Filtering: Lifeblood of The Social Web - ReadWriteWeb - 0 views

www.readwriteweb.com/...ative_filtering_social_web.php

internetwatch recommender

shared by Roger Chen on 01 Jul 08 - Cached
  • This, of course, relies on the fact that people's interests, preferences, and ideologies don't change too drastically over time.
  • A filtering system with preference-based recommendations, in essence, is the future of the social web.
  • The best implementations of a Collaborative Filtering (CF) system along with a preference based recommendation/discovery system that I have seen are always on music streaming and discovery sites.
  • ...3 more annotations...
  • As you can see from above, it is certainly possible to have a good collaborative filtering system without a recommendation engine
  • Collaborative Filtering (Wikipedia definition) is a mechanism used to filter large amounts of information by spreading the process of filtering among a large group of people.
  • The important thing, one that not many social sites realize, is that a (CF) system that doesn't automatically match content to your preferences, is inherently flawed. The reason for this is simple: Unless you can achieve perfect diversity and independence of opinion, one point of view will always dominate another on a particular platform. The dominant point of view on the social web is a left-leaning one, and without the ability to get the most appropriate pieces of content to the people that care most about them, the right-wing point of view gets buried almost every time.
Roger Chen

Using Aardvark - Duke Listens! - 0 views

blogs.sun.com/...using_aardvark

recommender social media

shared by Roger Chen on 25 Apr 08 - Cached
  • For one thing, we are using content analysis, classification and autotagging to help identify relevant content. We use incoming links and attention to determine how much authority a particular entry has on a topic.
    • Roger Chen
      Roger Chen on 25 Apr 08
       
      Attention? How to find and measure the attention?
  • Roger Chen on 25 Apr 08
     
    Project Aura - a blog recommender.
Roger Chen

Google Makes us Stupid « Synthèse - 0 views

synthese.wordpress.com/...googlemakesusstupid

internetwatch recommender thinking

shared by Roger Chen on 24 Jun 08 - Cached
  • One reason that is often given for building recommenders, particularly in the context of a digital library, is that it is supposed to help address the information overload problem. However, one can easily argue that the converse is true.
  • Thus, a recommender actually adds to the information overload problem and thus exacerbates the attention deficit problem that Carr complains about in his article.
  • perhaps we should focus on what’s in front of us instead of fretting about whether we’ve reached 100% recall.
Roger Chen

GroupLens Research - 0 views

www.grouplens.org

academia people data mining recommender

shared by Roger Chen on 26 Jun 08 - Cached
  • Roger Chen on 26 Jun 08
     
    GroupLens is a research lab in the Department of Computer Science and Engineering at the University of Minnesota. We conduct research in several areas, including: * recommender systems * online communities * mobile and ubiquitious technologies * digital libraries * local geographic information systems
Roger Chen

Recommendation Nation - Learning to love customers like you. - 0 views

www.technologyreview.com/...page1

internetwatch recommender

shared by Roger Chen on 10 May 08 - Cached
  • Roger Chen on 10 May 08
     
    The focus of digital personalization has shifted from what I am interested in now to what I might be interested in next.
Roger Chen

Pyflix - Trac - 0 views

pyflix.python-hosting.com

netflix python

shared by Roger Chen on 29 Jul 08 - Cached
  • Roger Chen on 29 Jul 08
     
    Pyflix is a small package written in Python that provides an easy entry point for getting up and running in the Netflix Prize competition. It combines an efficient storage scheme with an intuitive high-level API that allows contestants to focus on the real problem, the recommendation system algorithm. To get started with Pyflix, keep reading.
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