GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Multidimensional Recommender Systems: A Data Warehousing Approach
WELCOM '01 Proceedings of the Second International Workshop on Electronic Commerce
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
AWESOME: a data warehouse-based system for adaptive website recommendations
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Seven pitfalls to avoid when running controlled experiments on the web
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Investigation of various matrix factorization methods for large recommender systems
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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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.