Hi Ted, Sure. I'll continue reading and try examples in later chapters. Thanks.
regards, Vinod On Thu, Dec 8, 2011 at 7:53 PM, Ted Dunning <[email protected]> wrote: > This is a fair statement of the traditional way of doing business for > *small* models of the sort used in classification. The insistence on using > serialization is kind of silly since there are many down-sides to Java > serialization and it is becoming rare for systems that need to serialize > large amounts of data to use Java serialization. > > The fact is, however, that this is not general practice with > recommendations. It is common to do lots of off-line computation that you > could characterize as "learning", and it is common to save the results of > this off-line computation for later deployment, but it is also common to do > the learning on the fly since it is generally pretty trivial stuff. > > The earliest examples highlight the simpler approach. Keep going to see > more interesting examples. > > On Thu, Dec 8, 2011 at 6:46 AM, Vinod <[email protected]> wrote: > > > I'll use the first example from Chapter 2 of your book to clarify what I > > mean by training:- > > > > Following code trains the recommender:- > > DataModel model = new FileDataModel(new File("intro.csv")); > > > > UserSimilarity similarity = new PearsonCorrelationSimilarity(model); > > UserNeighborhood neighborhood = > > new NearestNUserNeighborhood(2, similarity, model); > > > > Recommender recommender = new GenericUserBasedRecommender( > > model, neighborhood, similarity); > > > > At this point, recommender is trained on preferences of users 1 to 5 in > > intro.csv. > > > > We should now be able to serialize() this recommender instance into a > file, > > say "Movie Recommender.model" using steps mentioned here ( > > > http://java.sun.com/developer/technicalArticles/Programming/serialization/ > > ) > > > > All we need to do now is deploy "Movie Recommender.model" to production. > > > > If I understand the behavior correctly, this model should now be able to > > predict recommendation for a new user. > > >
