I understand that, I guess what I am confused is the implementation of merging the two similarity metrics in code. For example I apply LogLikelihoodSimilarity for both item and hobby, and I have 2 UserSimilarity metrics. Then from there I am unsure of how to combine the two.
On 18 March 2013 21:49, Sean Owen <sro...@gmail.com> wrote: > I'm not sure what you mean. The only thing I am suggesting to combine are > two similarity metrics, not data or recommendations. > You combine metrics by multiplying their values. > > > On Mon, Mar 18, 2013 at 12:54 PM, Agata Filiana <a.filian...@gmail.com > >wrote: > > > 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 <sro...@gmail.com> 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 <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 > > > > * > > > > > > > > > > > > > > > -- > > *Agata Filiana > > * > > > -- *Agata Filiana *
