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 <[email protected]> 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. > >
