IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v22y2011i1p99-117.html
   My bibliography  Save this article

REQUEST: A Query Language for Customizing Recommendations

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.1100.0274
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.1100.0274?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. Levent V. Orman, 2016. "Information markets over trust networks," Electronic Commerce Research, Springer, vol. 16(4), pages 529-551, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Olãh Judit & Erdei Edina & Popp Jozsef, 2017. "Applying Big Data Algorithms For Sales Data Stored In Sap Hana," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 453-461, July.
    2. Huang, Chun-Che & (Bill) Tseng, Tzu-Liang & Li, Ming-Zhong & Gung, Roger R., 2006. "Models of multi-dimensional analysis for qualitative data and its application," European Journal of Operational Research, Elsevier, vol. 174(2), pages 983-1008, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orisre:v:22:y:2011:i:1:p:99-117. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.