Hi, Thank Sean for the response. I like the idea of multiplying the similarity metric based on user properties with the one based on CF data. I understand that I have to create a seperate similarity metric - can I do this with the help of Mahout or does this have to be done seperately, as in I have to implement my own similarity measure? It would be great if there is some clue on how I get this started. Is this somehow similar to the subject of *Injecting domain-specific information* in the book Mahout in Action (with the example of the gender-based item similarity metric)?
And also how can I multiply the two results - will this affect the result of the evaluation of the recommender system? Or it should be normalized in a way? Thank you and sorry for the basic questions. Regards, Agata Filiana On 16 March 2013 13:41, Sean Owen <[email protected]> wrote: > There are many ways to think about combining these two types of data. > > If you can make some similarity metric based on age, gender and interests, > then you can use it as the similarity metric in > GenericBooleanPrefUserBasedRecommender. You would be using both data sets > in some way. Of course this means learning a whole different similarity > metric somehow. A variant on this is to make a similarity metric based on > user properties, and also use one based on CF data, and multiply them > together to make a new combined similarity metric for this approach. This > might work OK. > > It can also work to treat age and gender and other features as categorical > features, and then model them as 'items' that the user interacts with. They > would not have much of an effect here given how many items there are. In > other models like ALS-WR you can weight these pseudo-items much more highly > and get the desired effect to a degree. > > > > On Fri, Mar 15, 2013 at 4:37 PM, Agata Filiana <[email protected] > >wrote: > > > Hi, > > > > I'm fairly new to Mahout. Right now I am experimenting Mahout by trying > to > > build a simple recommendation system. What I have is just a boolean data > > set, with only the userID and itemID. I understand that for this case I > > have to use GenericBooleanPrefUserBasedRecommender - which I have and > works > > fine. > > > > Apart from the userID and itemID data, I also have the user's attributes > > (their age, gender, list of interests). I would like to combine this into > > the recommendation system to increase the performance of the recommender. > > Is this possible to do or am I trying something that does not make sense? > > > > It would be great if you can give me any inputs or ideas for this. (Or > any > > good read based on this matter) > > > > Thank you! > > > > Regards, > > > > *Agata Filiana* > > Erasmus Mundus Student > > > -- *Agata Filiana *
