Integrating OLAP and recommender systems: an evaluation perspective

The integration of OLAP with web-search technologies is a promising research topic. Recommender systems are popular web-search mechanisms, because they can address information overload and provide personalization of results. Nevertheless, the evaluation of recommender systems is a challenging task. In this paper, we propose a novel framework for evaluating recommender systems, which is multidimensional and takes into account for the multiple facets of the recommendation algorithms, data sets and performance measures. Emphasis is placed on supporting business applications of recommender systems, notably e-commerce, by allowing analysts to perform ad-hoc analysis and use popular online analytical processing (OLAP) operations. Combined with support for visual analysis, action such as drill-down or slice/dice allow assessment of the performance of recommendations in terms of business objectives. We describe a detailed methodology for designing and developing the proposed multidimensional ...

• Type: Journal
• Year: 2010
• Where: DOLAP
• Authors: Artus Krohn-Grimberghe, Alexandros Nanopoulos, Lars Schmidt-Thieme
• Keywords: DOLAP 2010, Information Technology, Performance, Promising Research Topic, Recommender Systems