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
