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Deploying recommender system for the masses

Published: 24 February 2014 Publication History

Abstract

Many small and mid-sized e-businesses wish to integrate a recommender system into their website. Integrating an existing recommender system to a website often requires certain expertise and programming efforts, thus incurs substantial investments and may not be justified by the added value of the recommender system. This demo presents a solution for integrating a recommender system as a service to an existing e-business without any programming efforts. The integration method is analogue to the way of the Google AdSense integration and the business model is adapted from the advertisements world. Initial feedback from real website owners indicates that such integration has a great benefit for both sides; the website owner and the Recommender System (RS) provider.

References

[1]
Masse, Mark. REST API design rulebook. O'Reilly, 2011.
[2]
Schafer, J. Ben, Joseph Konstan, and John Riedi. "Recommender systems in e-commerce." Proceedings of the 1st ACM conference on Electronic commerce. ACM, 1999.
[3]
Davis, Harold. Google advertising tools: Cashing in with AdSense, AdWords, and the Google APIs. O'reilly, 2006.

Cited By

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  • (2025)From Data to Decisions: The Power of Machine Learning in Business RecommendationsIEEE Access10.1109/ACCESS.2025.353269713(17354-17397)Online publication date: 2025
  • (2019)Phrase-guided attention web article recommendation for next clicks and viewsProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3341161.3342869(315-324)Online publication date: 27-Aug-2019
  • (2017)Numerical similarity-aware data partitioning for recommendations as a serviceProceedings of the Symposium on Applied Computing10.1145/3019612.3019676(887-892)Online publication date: 3-Apr-2017
  • Show More Cited By

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    cover image ACM Conferences
    IUI '14 Companion: Companion Proceedings of the 19th International Conference on Intelligent User Interfaces
    February 2014
    100 pages
    ISBN:9781450327299
    DOI:10.1145/2559184
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 24 February 2014

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    Author Tags

    1. collaborative filtering
    2. integration
    3. recommender system as a service

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    • Demonstration

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    IUI'14
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    Overall Acceptance Rate 746 of 2,811 submissions, 27%

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    IUI '25

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    Cited By

    View all
    • (2025)From Data to Decisions: The Power of Machine Learning in Business RecommendationsIEEE Access10.1109/ACCESS.2025.353269713(17354-17397)Online publication date: 2025
    • (2019)Phrase-guided attention web article recommendation for next clicks and viewsProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3341161.3342869(315-324)Online publication date: 27-Aug-2019
    • (2017)Numerical similarity-aware data partitioning for recommendations as a serviceProceedings of the Symposium on Applied Computing10.1145/3019612.3019676(887-892)Online publication date: 3-Apr-2017
    • (2016)Anytime Algorithms for Recommendation Service ProvidersACM Transactions on Intelligent Systems and Technology10.1145/28354967:3(1-26)Online publication date: 11-Feb-2016
    • (2015)RecSys Challenge 2015 and the YOOCHOOSE DatasetProceedings of the 9th ACM Conference on Recommender Systems10.1145/2792838.2798723(357-358)Online publication date: 16-Sep-2015
    • (2014)Configuring and monitoring recommender system as a serviceProceedings of the 8th ACM Conference on Recommender systems10.1145/2645710.2645713(363-364)Online publication date: 6-Oct-2014

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