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. > > > > >
