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REQUEST: A Query Language for Customizing Recommendations

Author

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  • Gediminas Adomavicius

    (Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455)

  • Alexander Tuzhilin

    (Information, Operations & Management Sciences Department, Stern School of Business, New York University, New York, New York 10012)

  • Rong Zheng

    (Department of Information Systems, Business Statistics and Operations Management, Business School, Hong Kong University of Science and Technology, Kowloon, Hong Kong)

Abstract

Initially popularized by Amazon.com, recommendation technologies have become widespread over the past several years. However, the types of recommendations available to the users in these recommender systems are typically determined by the vendor and therefore are not flexible. In this paper, we address this problem by presenting the recommendation query language REQUEST that allows users to customize recommendations by formulating them in the ways satisfying personalized needs of the users. REQUEST is based on the multidimensional model of recommender systems that supports additional contextual dimensions besides traditional User and Item dimensions and also OLAP-type aggregation and filtering capabilities. This paper also presents the recommendation algebra RA, shows how REQUEST recommendations can be mapped into this algebra, and analyzes the expressive power of the query language and the algebra. This paper also shows how users can customize their recommendations using REQUEST queries through a series of examples.

Suggested Citation

  • Gediminas Adomavicius & Alexander Tuzhilin & Rong Zheng, 2011. "REQUEST: A Query Language for Customizing Recommendations," Information Systems Research, INFORMS, vol. 22(1), pages 99-117, March.
    • Handle: RePEc:inm:orisre:v:22:y:2011:i:1:p:99-117
    DOI: 10.1287/isre.1100.0274
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    References listed on IDEAS

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    1. Helen Thomas & Anindya Datta, 2001. "A Conceptual Model and Algebra for On-Line Analytical Processing in Decision Support Databases," Information Systems Research, INFORMS, vol. 12(1), pages 83-102, March.
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    Cited by:

    1. Thushari Silva & Jian Ma & Chen Yang & Haidan Liang, 2015. "A profile-boosted research analytics framework to recommend journals for manuscripts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(1), pages 180-200, January.
    2. Daniel Zeng & Yong Liu & Ping Yan & Yanwu Yang, 2021. "Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1608-1623, October.
    3. Levent V. Orman, 2016. "Information markets over trust networks," Electronic Commerce Research, Springer, vol. 16(4), pages 529-551, December.
    4. Abhijeet Ghoshal & Syam Menon & Sumit Sarkar, 2015. "Recommendations Using Information from Multiple Association Rules: A Probabilistic Approach," Information Systems Research, INFORMS, vol. 26(3), pages 532-551, September.

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