International Business Machines Corporation
EVALUATING A RECOMMENDER SYSTEM FOR DATA PROCESSING

Abstract:

An approach is provided for evaluating a recommender system. A user's purchase date of an item and attributes of the item are extracted from a test set. Drop probabilities are assigned to the attributes. Using bootstrapping with aggregation, queries for the user and the item are generated by omitting one or more attributes from each of the queries according to the drop probabilities. Data that became available to the recommender system after the purchase date is identified. Without using the identified data and using data that became available to the recommender system before the purchase date, ranked item recommendation sets for the queries are generated. Similarity at rank K (SIM@K) values for the recommendation sets are calculated. Average SIM@K values are calculated over multiple users specified in the test set. Based on the average SIM@K values, the performance of the recommender system is evaluated.