Hi Bhaskar, I used to have same problem and implemented a similar evaluator as you have mentioned. It's really much faster than original version. Moreover in my version splitted dataset is an input since I use it for several tests. I may share the code or we may work on this.
Regards. 2015-06-05 9:51 GMT+03:00 Bhaskar Bagchi <[email protected]>: > Hi, > > I was working with the GenericRecommenderIRStatsEvaluator when I noticed > that the GenericRelevantItemsDataSplitter.java class only removes the *good > user preferences* for the user for which the evaluation is being run and > keeps all the other data points and builds the data point for every user > separately before evaluation. This makes the loop O(n^2). > > Why don't we make a single split of data using the percentage provided by > the user and build a single recommender model using this split, which can > be used to evaluate all the users? This will make the evaluator pretty > fast. > > Can anyone help me with making a single data split for evaluation? > > -- > Thanks and Regards > > Bhaskar Bagchi > Data Science Intern > TinyOwl >
