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Scientific Recommender Systems Prototype

12 Dec

Today, I want to introduce my ideas for recommenders for my prototype to you. Basically, I want to create a simple web application with JSP which provides different recommenders and corresponding visualizations based on the given mysql dump. Following is a list of the recommenders that I am thinking of with a few thoughts on the implementation and sketches for possible visualizations.

1.) recommend papers based on citations as boolean preferences between papers (collaborative filtering)

implementation with mahout: Create a datamodel based on boolean preferences (as in an association exist or does not) and then run the recommender with different similarity metrics contained in mahout (that can work with boolean preferences), evaluate and compare them.

2.) recommend papers based on cocitation

implementation: If I understand the contents of the co_citation view correctly (count of the cocitations between two papers), this would simply be a maximum search with the ID of the input paper as one of the IDs in the view.

possible visualization:

(A denotes the recommendation, # the number of cocitations between the input paper and A)

3.) recommend papers based on bibliographic coupling

implementation: Again, if I understand the contents of the bib_coupling view correctly (count of the bibliographic couplings between two papers), this would simply be a maximum search with the ID of the input paper as one of the IDs in the view.

possible visualization:

(A denotes the recommendation, # the number of bibliographic couplings between the input paper and A)

4.) recommend papers based on common keywords

implementation with mahout: Create an item-based recommender and create an ItemSimilarity class which computes the similarity between two papers based on their shared keywords.

5.) recommend people based on co-authorship (collaborative filtering)

implementation with mahout: Co-authorship as preferences between authors (so people who have often written together have a high preference for each other), user-based recommender to find similar people

6.) recommend people based on event participation

implementation with mahout: Again co-authorship as preferences between authors, item-based recommender (create an ItemSimilarity class which computes the similarity between two authors based on their common event participations), recommendations should then be something like authors who often participated in the same events as the input author and/or his co-authors but never wrote a paper together with the input author

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2 Comments

Posted by on 12.12.2011 in Java, Mahout, Recommender Systems, Seminar Phase

 

Tags: , ,

2 responses to “Scientific Recommender Systems Prototype

  1. wollepb

    13.12.2011 at 10:36

    What about recommendations based on co-authorship or mediated co-authorship? Also recommendations based on event participation or affiliations would be interesting…

     
  2. Jan

    13.12.2011 at 16:58

    I edited the post and added two more ideas for recommenders which recommend people (instead of papers like the other four recommenders) based on co-authorship and event participation

     

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