Yes. You could define item similarity based on your movie category in your data: 1 if they're in the same category, 0 if not. That's very simplistic. And you'd have to write it yourself. But that's what it is referring to.
However, you need more than an item similarity metric to make a recommender. You need user-item preferences. Without that you have nothing to make recommendations from. item-item similarity doesn't somehow magically tell you what users like what item. On Tue, Mar 13, 2012 at 7:57 PM, Ahmed Abdeen Hamed <ahmed.elma...@gmail.com> wrote: > Sorry if my questions are hard to understand. > > Let's start all over... > > Do we have an example that explains the following paragraph the in MiA > book? > > "Or recall that item-based recommenders require some notion of similarity > > between two given items. This similarity is encapsulated by an > ItemSimilarity implementation. > > So far, implementations have derived similarity from user preferences > > only—this is classic collaborative filtering. But there’s no reason the > implementation > > couldn’t be based on item attributes. For example, a movie recommender might > > define item (movie) similarity as a function of movie attributes like genre, > director, > > actor, and year of release. Using such an implementation within a > traditional item" > > > This is the part that I am trying to understand and have a solution for. > > Thanks, > > -Ahmed > > > > On Tue, Mar 13, 2012 at 2:08 PM, Sean Owen <sro...@gmail.com> wrote: >> >> OK, you have some users. You have some items, and those items have >> attributes. >> >> Nothing here connects users to items though, so how can any process >> estimate any additional user-item connections? >> >> You could compute item-item similarities, but that doesn't resolve this. >> >> Sorry I am really confused -- you have been talking about queries but >> saying you are not using any search. It's hard to help. >> >
