Hmm. Cool thanks, looks like I got to do a lot of reading. On Fri, Oct 1, 2010 at 12:07 AM, Sean Owen <[email protected]> wrote:
> Sure. I would suggest you create an ItemSimilarity implementation which > loads this additional information, and constructs some formula for > similarity based on whether movies share genre, actors, etc. For example > maybe being in the same genre is worth +0.1 similarity. Maybe same actor is > worth +0.2. > > (Of course you could do as much work as you like to construct an even > smarter function.) > > Then you simply use this with your existing DataModel and a > GenericItemBasedSimilarity. > > That's the most direct way to incorporate this info. > > On Fri, Oct 1, 2010 at 12:14 AM, web service <[email protected]> wrote: > > > I have got the group lens example working. Had a couple of doubts though > - > > The dataset in grouplens has movieid, userid and the corresponding > ratings. > > However a rating is meant to rate a movie but there are other things > > related > > to a movie to which the rating contributes. > > For example, the actors, directors, movie genre or may be the year of > > release etc. > > > > So, is there any way to capture this relationship and then generate > > recommendation. > > > > Any suggestions, ideas about how to represent data or vector and then > > compute recommendations or how it is done usually etc. would be nice. > > > > -Mac > > >
