There is a difference between the recommender and the similarity metric it uses. My suggestion was to either use your item data with the recommender and hobby data with the similarity metric, or, use both in the similarity metric by making a combined metric.
On Mon, Mar 18, 2013 at 9:44 AM, Agata Filiana <a.filian...@gmail.com>wrote: > I understand how it works logically. However I am having problem > understanding about the implementation of it and how to get the final > outcome. > Say the user's attribute is Hobbies: hobby1,hobby2,hobby3 > So I would make the similarity metric of the users and hobbies. > > Then for the CF, using Mahout's GenericBooleanPrefUserBasedRecommender with > the boolean data set (userID and itemID). > > Then somehow combine the two? > > But at the end, my goal is to recommend the items in the second data set > (the itemID, not recommend the hobbies) - does this make sense? Or am I > confusing myself? > > Agata > > > On 18 March 2013 14:23, Sean Owen <sro...@gmail.com> wrote: > > > You would have to make up the similarity metric separately since it > depends > > entirely on how you want to define it. > > The part of the book you are talking about concerns rescoring, which is > not > > the same thing. > > Combine the similarity metrics, I mean, not make two recommenders. Make a > > metric that is the product of two other metrics. Normalize both of those > > metrics to the range [0,1]. > > > > Sean > > > > > > On Mon, Mar 18, 2013 at 6:51 AM, Agata Filiana <a.filian...@gmail.com > > >wrote: > > > > > 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 <sro...@gmail.com> 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 < > a.filian...@gmail.com > > > > >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 > > > * > > > > > > > > > -- > *Agata Filiana > * >
