APPLYING INFORMATION RETRIEVAL FOR MARKET BASKET RECOMMENDER SYSTEMS

摘要

Coded data sets form the basis for many well known applications from healthcare prospective payment system to recommender systems in online shopping. Previous studies on coded data sets have introduced methods for the analysis of rather small data sets. This study proposes applying information retrieval methods for enabling high performance analysis of data masses that scale beyond traditional approaches. An essential component in today's data warehouses to which coded data sets are collected is a database management system (DBMS). This study presents experimental results how information retrieval indexes scale and outperform common database schemas with a leading commercial DBMS engine in analysis of coded data sets. The results show that flexible analysis of hundreds of millions of coded data sets is possible with a regular desktop hardware.