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 >
