Thanks again for the response! Perhaps this is a search problem I will not disagree. However, I am not using any search of any sort. I have a bunch of items that I need to derive "the implicit preferences" among them using their attributes (genre, director, actor, etc). And, right now, I don't have any IR scores which is what I try to compute. Just item-id, and user-id. And my final goal is to have the following fields in my file to compute similarities and make recommendations:
item-id, user-id, score 100, 700, 0.787 100, 767, 0.653 . . . That's all I have and I don't intend to use any search components. Sorry if I am not making this hard for you to understand. -Ahmed On Tue, Mar 13, 2012 at 1:43 PM, Sean Owen <[email protected]> wrote: > Before I answer, I want to make sure we're on the same page. You are > definitely describing a search problem. Was my guess at how you are > also adding in something recommender-related accurate? > > Otherwise we may be talking past each other again. > > On Tue, Mar 13, 2012 at 5:35 PM, Ahmed Abdeen Hamed > <[email protected]> wrote: > > Thanks Sean and Ted! > > > > Let me explain how I got here in the first place. I have an interest in > > content-based similarities. When I read the two sections in the MiA book > > about that, I got some hints. I don't have user preferences and was > trying > > to use the content-based similarities in its place as the book explains. > > Therefore, my question is really about computing this similarity from > item > > attributes. How can I do that without the use of search queries? > > > > Thanks, > > > > -Ahmed >
