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 On Tue, Mar 13, 2012 at 10:36 AM, Ted Dunning <[email protected]> wrote: > This is search, not recommendation. > > For search, you need to build and index (which can be built off-line). In > the process of building that index, you can propagate content terms across > highly similar (behaviorally) items and you can include references to and > from similar items. > > Content-based recommendation uses content attributes on items to refine > the item-item similarities and uses content attributes on users to help > access those similarities. Often one uses a search engine such as solr to > augment the real-time side of the implementation. > > > On Tue, Mar 13, 2012 at 9:28 AM, Ahmed Abdeen Hamed < > [email protected]> wrote: > >> Hi Sean, >> >> I did some reading before writing so I can ask more specific questions. >> The >> MiA book has a couple of sections that cover content-based. The move >> attributes examples make sense. However, it appears to me that the >> similarity can not be computed offline. This is because the similarity is >> depended on a user query that will be entered in real time. For instance, >> assume that we have two different movies in our database we would like to >> recommend, among other movies along with the genre: >> >> The Matrix, Action Adventure >> The Matrix of Power, Documentary >> Matrix Method, Sports and Fitness >> The Matrix Reloaded, Action Adventure >> >> Now if the user query was "matrix sports" the similarity will be higher >> for >> Matrix Method movie than the Matrix Reloaded. But these similarities will >> only be available after the user enters the query. >> >> My question now is: is there a way to compute these similarities offline? >> >> Thanks very much, >> >> -Ahmed >> >> >> >> >> >> On Tue, Mar 6, 2012 at 5:14 PM, Sean Owen <[email protected]> wrote: >> >> > Sure, you just write your own ItemSimilarity implementation based on >> > the content, whatever that may be. what you do there is mostly up to >> > you; there's not a framework for this. >> > >> > >> > >
