In this case, would be correct if I somehow "loop" through the item data and the hobby data and then combine the score for a pair of users?
I am having trouble in how to combine both similarity into one metric, could you possibly point me out a clue? Thank you On 18 March 2013 14:54, Sean Owen <[email protected]> wrote: > 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 <[email protected] > >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 <[email protected]> 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 <[email protected] > > > >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 <[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 > > > > * > > > > > > > > > > > > > > > -- > > *Agata Filiana > > * > > > -- *Agata Filiana *
