NDLI: Integrating OLAP and recommender systems: an evaluation perspective

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Integrating OLAP and recommender systems: an evaluation perspective

Content Provider	ACM Digital Library
Author	Schmidt-Thieme, Lars Nanopoulos, Alexandros Krohn-Grimberghe, Artus
Abstract	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 framework, and provide insights about its applications. Our experimental results, using a research prototype, demonstrate the ability of the proposed framework to comprise an effective way for evaluating recommender systems.
Starting Page	85
Ending Page	92
Page Count	8
File Format	PDF
ISBN	9781450303835
DOI	10.1145/1871940.1871959
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-10-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Multidimensional Integration Exploratory data analysis Olap Performance analysis Data warehouse Recommendation Recommender systems
Content Type	Text
Resource Type	Article

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