skip to main content
10.1145/1871940.1871959acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article

Integrating OLAP and recommender systems: an evaluation perspective

Published: 30 October 2010 Publication History

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.

References

[1]
G. Adomavicius, R. Sankaranarayanan, S. Sen, and A. Tuzhilin. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst., 23(1):103--145, 2005.
[2]
G. Adomavicius and A. Tuzhilin. Multidimensional recommender systems: A data warehousing approach. In WELCOM '01: Proceedings of the Second International Workshop on Electronic Commerce, pages 180--192, London, UK, 2001. Springer-Verlag.
[3]
R. Bell, Y. Koren, and C. Volinsky. Modeling relationships at multiple scales to improve accuracy of large recommender systems. In KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 95--104, New York, NY, USA, 2007. ACM.
[4]
J. Bennett, S. Lanning, and N. Netflix. The netflix prize. In In KDD Cup and Workshop in conjunction with KDD, 2007.
[5]
T. Crook, B. Frasca, R. Kohavi, and R. Longbotham. Seven pitfalls to avoid when running controlled experiments on the web. In KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1105--1114, New York, NY, USA, 2009. ACM.
[6]
GroupLens. Movielens data sets. http://www.grouplens.org/node/73.
[7]
C. Hayes, P. Massa, P. Avesani, and P. Cunningham. An on-line evaluation framework for recommender systems. In In Workshop on Personalization and Recommendation in E-Commerce (Malaga. Springer Verlag, 2002.
[8]
J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst., 22(1):5--53, 2004.
[9]
Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 426--434, New York, NY, USA, 2008. ACM.
[10]
Y. Koren. Collaborative filtering with temporal dynamics. In KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 447--456, New York, NY, USA, 2009. ACM.
[11]
A. Krohn-Grimberghe, A. Nanopoulos, and L. Schmidt-Thieme. A novel multidimensional framework for evaluating recommender systems. In Proceedings of the ACM RecSys 2010 Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces (UCERSTI). CEUR-WS, to appear.
[12]
P. Resnick, N. Iacovou, M. Sushak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In 1994 ACM Conference on Computer Supported Collaborative Work Conference, pages 175--186, Chapel Hill, NC, 10/1994 1994. Association of Computing Machinery, Association of Computing Machinery.
[13]
G. Takács, I. Pilászy, B. Németh, and D. Tikk. Investigation of various matrix factorization methods for large recommender systems. In NETFLIX '08: Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition, pages 1--8, New York, NY, USA, 2008. ACM.
[14]
A. Thor and E. Rahm. Awesome: a data warehouse-based system for adaptive website recommendations. In VLDB '04: Proceedings of the Thirtieth international conference on Very large data bases, pages 384--395. VLDB Endowment, 2004.

Cited By

View all
  • (2022)SSLE: A framework for evaluating the “Filter Bubble” effect on the news aggregator and recommendersWorld Wide Web10.1007/s11280-022-01031-425:3(1169-1195)Online publication date: 8-Mar-2022
  • (2018)A Model-Driven Approach to Evolve Recommender SystemsProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3267457(169-172)Online publication date: 16-Oct-2018
  • (2016)A Recommendation System Using OLAP Approach2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0109(622-625)Online publication date: Oct-2016
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
DOLAP '10: Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
October 2010
112 pages
ISBN:9781450303835
DOI:10.1145/1871940
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2010

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. data warehouse
  2. exploratory data analysis
  3. integration
  4. multidimensional
  5. olap
  6. performance analysis
  7. recommendation
  8. recommender systems

Qualifiers

  • Research-article

Conference

CIKM '10

Acceptance Rates

Overall Acceptance Rate 29 of 79 submissions, 37%

Upcoming Conference

CIKM '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)0
Reflects downloads up to 10 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2022)SSLE: A framework for evaluating the “Filter Bubble” effect on the news aggregator and recommendersWorld Wide Web10.1007/s11280-022-01031-425:3(1169-1195)Online publication date: 8-Mar-2022
  • (2018)A Model-Driven Approach to Evolve Recommender SystemsProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3267457(169-172)Online publication date: 16-Oct-2018
  • (2016)A Recommendation System Using OLAP Approach2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0109(622-625)Online publication date: Oct-2016
  • (2014)Movie Recommendation Using OLAP and Multidimensional Data ModelComputer Information Systems and Industrial Management10.1007/978-3-662-45237-0_21(209-218)Online publication date: 2014
  • (2010)DOLAP 2010 workshop summaryProceedings of the 19th ACM international conference on Information and knowledge management10.1145/1871437.1871792(1973-1974)Online publication date: 26-Oct-2010

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media