RecBench: Benchmarks for Evaluating Performance of Recommender System Architectures
Traditionally, recommender systems have been "Hand-built", implemented as custom applications hard-wired to a particular recommendation task. Recently, the database community has begun exploring alternative DBMS-based recommender system architectures, whereby a database both stores the recommender system data (e.g., ratings data and the derived recommender models) and generates recommendations using SQL queries. In this paper, the authors present a comprehensive experimental comparison of both architectures. They define a set of benchmark tasks based on the needs of a typical recommender-powered e-commerce site.