This is small enough that you can fit this into memory on one machine, and you do not need Hadoop.
I would simply start with a GenericBooleanPrefItemBasedRecommender, and attach it to a LogLikelihoodSimilarity similarity metric. Wrap the LogLikelihoodSimilarity in a CachingItemSimilarity. You can feed your associations in anyway you want, but one easy way is as a CSV file of "userID,itemID" and a FileDataModel. This ought to work pretty well for you, but is just a starting point. On Sun, Sep 11, 2011 at 6:01 PM, Manju <[email protected]> wrote: > Dear Mahout team, > > Need some advice. The books "Mahout/Hadoop in action" and online information > has helped me digest the basic concepts and setup a single node hadoop + > mahout (run examples/write test programs/build etc.). > > I am prototyping a solution for an analytics problem using > User/Itemrecommender structure (to start with). I have a list of 300 thousand > users who have bought (on average) 10 items from a finite set of 300 items. I > dont have individual preferences for each item bought. As the items are > expensive, require pre-buy research and have very low complaint/returns, I am > assuming that users liked the items they bought (for first iteration till I > get more sophisticated data). > > Any advice on how best to approach the scenario with item or user based > recommendation (given the lack of spread in ratings/preferences)? > > Appreciate your advice. > Manju >
