On Sat, Jan 30, 2010 at 2:34 AM, Vinicius Carvalho <[email protected]> wrote: > I'm trying the 5.1.10 the latest one available at maven repositories, > running it right now, since it takes a while, I'll inform of the results
OK but this would be something you can check in your table right now. No columns should be nullable, or have nulls. If they do, that's the problem. > At first I'm just creating the slopeonerecommender. did not even get to the > actual code, all that time is used on the construction of the object OK then it's the time spent in building diffs. > You mean for the DiffStorage right? The datamodel would be good to be at > JDBC right? I'm interested in item2item recommendations. I did this before For both, 10M ratings isn't terribly big. I think you can get it into memory in 2GB, plus the diffs, if you cap the number of diffs at some reasonable value. > using taste by hand by computing the SimilarityMatrix and storing it on DB. > (I used as reference the book Collective Intelligence in action) and it > worked fine. Just the Similarity Matrix took a while to be recalculated by > it was a batch job running every hour. After that computing recomendations > was just a breeze. You mean you are interested in item-based recommenders, or recommending items to other items? Slope-one wouldn't have anything to do with item-item similarities, it works a bit differently. yes you could pre-compute similarities and use them with a custom ItemSimilarity implementation which reads from a DB table, and use that with GenericItemBasedRecommender. You could also do the similarity calculations with something like PearsonCorrelationSimilarity, and store that in the DB, and proceed with the above. Again, you'd have to write a little code but pretty easy. Or you could skip the DB altogether and let it compute item-item similarities on the fly.
