I've frequently thought that there should be a way to tightly bind a
bunch of items together (i think of them as a "project," all researched
in one swoop). 
 
I'm not sure how this would be different from virtual inlinks/outlinks,
or even what the distinction might be...


  _____  

        From: [email protected]
[mailto:[EMAIL PROTECTED] On Behalf Of Amir Michail
        Sent: Saturday, October 07, 2006 9:02 PM
        To: [email protected]
        Subject: [ydn-delicious] subwebs and better link recommendations
        
        

        Hi,
        
        What do you think of the following?
        
        * when entering a bookmark, you supply not only tags but also
"virtual
        inlinks" and "virtual outlinks"; for example, when bookmarking
        TeXmacs, you might supply LyX as a virtual inlink and several
TeXmacs
        resources pages as virtual outlinks.
        
        * these virtual inlinks and outlinks allow you to create your
own
        subweb that you can use to browse your bookmarks; for example,
when
        browsing the LyX bookmark in your subweb, you will see TeXmacs
as a
        virtual outlink
        
        * moreover, the combined virtual inlinks and outlinks of all
users
        provide an alternative view of the web, perhaps with more
interesting
        linking
        
        * when browsing links, you can click "more like this" or "fewer
like
        this"; the system keeps track of both of your liked and disliked
links
        
        * virtual inlinks and outlinks along with liked/disliked links
can be
        used to enhance personalized recommendations
        
        * for example, to compute the personalization score for some
link Y
        with respect to some user P's liked links U_1, ..., U_m and
disliked
        links V_1, ..., V_n, we could do the following:
        
        compute scores for Y's virtual inlinks from all users: say S_i
for
        X_i => Y (the stronger the implication, the higher the score)
        
        compute scores for Y's virtual outlinks from all users: say S'_i
for
        Y=>X_i (the stronger the implication, the higher the score)
        
        compute the personalization score for Y with respect to user P
as follows:
        
        sum S_i over U_i - sum S'_j over V_j
        
        Amir
        

         



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