IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v26y2015i3p532-551.html
   My bibliography  Save this article

Recommendations Using Information from Multiple Association Rules: A Probabilistic Approach

Author

Listed:
  • Abhijeet Ghoshal

    (College of Business, University of Louisville, Louisville, Kentucky 40292)

  • Syam Menon

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

  • Sumit Sarkar

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

Abstract

Business analytics has evolved from being a novelty used by a select few to an accepted facet of conducting business. Recommender systems form a critical component of the business analytics toolkit and, by enabling firms to effectively target customers with products and services, are helping alter the e-commerce landscape. A variety of methods exist for providing recommendations, with collaborative filtering, matrix factorization, and association-rule-based methods being the most common. In this paper, we propose a method to improve the quality of recommendations made using association rules. This is accomplished by combining rules when possible and stands apart from existing rule-combination methods in that it is strongly grounded in probability theory. Combining rules requires the identification of the best combination of rules from the many combinations that might exist, and we use a maximum-likelihood framework to compare alternative combinations. Because it is impractical to apply the maximum likelihood framework directly in real time, we show that this problem can equivalently be represented as a set partitioning problem by translating it into an information theoretic context—the best solution corresponds to the set of rules that leads to the highest sum of mutual information associated with the rules. Through a variety of experiments that evaluate the quality of recommendations made using the proposed approach, we show that (i) a greedy heuristic used to solve the maximum likelihood estimation problem is very effective, providing results comparable to those from using the optimal set partitioning solution; (ii) the recommendations made by our approach are more accurate than those made by a variety of state-of-the-art benchmarks, including collaborative filtering and matrix factorization; and (iii) the recommendations can be made in a fraction of a second on a desktop computer, making it practical to use in real-world applications.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:orisre:v:26:y:2015:i:3:p:532-551
    DOI: 10.1287/isre.2015.0583
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.2015.0583
    Download Restriction: no
    File URL: https://libkey.io/10.1287/isre.2015.0583?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. Kartik Hosanagar & Daniel Fleder & Dokyun Lee & Andreas Buja, 2014. "Will the Global Village Fracture Into Tribes? Recommender Systems and Their Effects on Consumer Fragmentation," Management Science, INFORMS, vol. 60(4), pages 805-823, April.
    2. 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.
    3. Ozlem Ergun & Gultekin Kuyzu & Martin Savelsbergh, 2007. "Reducing Truckload Transportation Costs Through Collaboration," Transportation Science, INFORMS, vol. 41(2), pages 206-221, May.
    4. Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.
    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. Jiawei Chen & Yinghui (Catherine) Yang & Hongyan Liu, 2021. "Mining Bilateral Reviews for Online Transaction Prediction: A Relational Topic Modeling Approach," Information Systems Research, INFORMS, vol. 32(2), pages 541-560, June.
    2. Qian Wang & Jijun Yu & Weiwei Deng, 2019. "An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations," Electronic Commerce Research, Springer, vol. 19(1), pages 59-79, March.
    3. Bae, Joonho & Park, Jinkyoo & Choi, Jeonghye & Bum Soh, Seung, 2023. "A recommending system for mobile games using the dynamic nonparametric model," Journal of Business Research, Elsevier, vol. 167(C).

    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. Xitong Li & Jörn Grahl & Oliver Hinz, 2022. "How Do Recommender Systems Lead to Consumer Purchases? A Causal Mediation Analysis of a Field Experiment," Information Systems Research, INFORMS, vol. 33(2), pages 620-637, June.
    2. Nathan M. Fong, 2017. "How Targeting Affects Customer Search: A Field Experiment," Management Science, INFORMS, vol. 63(7), pages 2353-2364, July.
    3. Dokyun Lee & Kartik Hosanagar, 2021. "How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems Through Purchase Stages?," Management Science, INFORMS, vol. 67(1), pages 524-546, January.
    4. Dokyun Lee & Kartik Hosanagar, 2019. "How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment," Service Science, INFORMS, vol. 30(1), pages 239-259, March.
    5. Dongwon Lee & Anandasivam Gopal & Sung-Hyuk Park, 2020. "Different but Equal? A Field Experiment on the Impact of Recommendation Systems on Mobile and Personal Computer Channels in Retail," Information Systems Research, INFORMS, vol. 31(3), pages 892-912, September.
    6. 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.
    7. Yi, Sangyoon & Kim, Dongyeon & Ju, Jaehyeon, 2022. "Recommendation technologies and consumption diversity: An experimental study on product recommendations, consumer search, and sales diversity," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    8. März, Armin & Lachner, Michael & Heumann, Christian G. & Schumann, Jan H. & von Wangenheim, Florian, 2021. "How You Remind Me! The Influence of Mobile Push Notifications on Success Rates in Last-Minute Bidding," Journal of Interactive Marketing, Elsevier, vol. 54(C), pages 11-24.
    9. Oliver Hinz & Jochen Eckert, 2010. "The Impact of Search and Recommendation Systems on Sales in Electronic Commerce," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 67-77, April.
    10. Gansterer, Margaretha & Hartl, Richard F. & Sörensen, Kenneth, 2020. "Pushing frontiers in auction-based transport collaborations," Omega, Elsevier, vol. 94(C).
    11. Miguel Godinho de Matos & Pedro Ferreira, 2020. "The Effect of Binge-Watching on the Subscription of Video on Demand: Results from Randomized Experiments," Information Systems Research, INFORMS, vol. 31(4), pages 1337-1360, December.
    12. Gavan J. Fitzsimons & Donald R. Lehmann, 2004. "Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses," Marketing Science, INFORMS, vol. 23(1), pages 82-94, September.
    13. Koehler, C.F. & Breugelmans, E. & Dellaert, B.G.C., 2010. "Consumer Acceptance of Recommendations by Interactive Decision Aids: The Joint Role of Temporal Distance and Concrete vs. Abstract Communications," ERIM Report Series Research in Management ERS-2010-041-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    14. Nils Engelbrecht & Tim-Benjamin Lembcke & Alfred Benedikt Brendel & Kilian Bizer & Lutz M. Kolbe, 2021. "The Virtual Online Supermarket: An Open-Source Research Platform for Experimental Consumer Research," Sustainability, MDPI, vol. 13(8), pages 1-25, April.
    15. repec:dgr:rugsom:04f04 is not listed on IDEAS
    16. Anthony Dukes & Lin Liu, 2016. "Online Shopping Intermediaries: The Strategic Design of Search Environments," Management Science, INFORMS, vol. 62(4), pages 1064-1077, April.
    17. O'Keefe, Robert M., 2016. "Experimental behavioural research in operational research: What we know and what we might come to know," European Journal of Operational Research, Elsevier, vol. 249(3), pages 899-907.
    18. Paul H. Y. Cheung & Yusufcan Masatlioglu, 2025. "Frame-dependent Random Utility," Papers 2502.00209, arXiv.org.
    19. Hallikainen, Heli & Luongo, Milena & Dhir, Amandeep & Laukkanen, Tommi, 2022. "Consequences of personalized product recommendations and price promotions in online grocery shopping," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).
    20. Joerß, Tom & Hoffmann, Stefan & Mai, Robert & Akbar, Payam, 2021. "Digitalization as solution to environmental problems? When users rely on augmented reality-recommendation agents," Journal of Business Research, Elsevier, vol. 128(C), pages 510-523.
    21. Peter J. Danaher & Isaac W. Wilson & Robert A. Davis, 2003. "A Comparison of Online and Offline Consumer Brand Loyalty," Marketing Science, INFORMS, vol. 22(4), pages 461-476, February.

    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:26:y:2015:i:3:p:532-551. 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.