research-article

Movie recommender system for profit maximization

Authors:

Avinatan Hassidim,

Ofer Weintraub,

Irit NetanelyAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 121 - 128

https://doi.org/10.1145/2507157.2507162

Published: 12 October 2013 Publication History

Abstract

Traditional recommender systems minimize prediction error with respect to users' choices. Recent studies have shown that recommender systems have a positive effect on the provider's revenue.

In this paper we show that by providing a set of recommendations different than the one perceived best according to user acceptance rate, the recommendation system can further increase the business' utility (e.g. revenue), without any significant drop in user satisfaction. Indeed, the recommendation system designer should have in mind both the user, whose taste we need to reveal, and the business, which wants to promote specific content.

We performed a large body of experiments comparing a commercial state-of-the-art recommendation engine with a modified recommendation list, which takes into account the utility (or revenue) which the business obtains from each suggestion that is accepted by the user. We show that the modified recommendation list is more desirable for the business, as the end result gives the business a higher utility (or revenue). To study possible reduce in satisfaction by providing the user worse suggestions, we asked the users how they perceive the list of recommendation that they received. Differences in user satisfaction between the lists is negligible, and not statistically significant.

We also uncover a phenomenon where movie consumers prefer watching and even paying for movies that they have already seen in the past than movies that are new to them.

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Cited By

Fu YXu XLiu HYu QDai HQuek T(2024)Joint Assortment and Cache Planning for Practical User Choice Model in Wireless Content Caching NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2023.3297987(1-13)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3297987
De Biasio AJannach DNavarin N(2024)Model-based approaches to profit-aware recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123642249:PBOnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123642
Jannach DAbdollahpouri H(2023)A survey on multi-objective recommender systemsFrontiers in Big Data10.3389/fdata.2023.11578996Online publication date: 22-Mar-2023
https://doi.org/10.3389/fdata.2023.1157899
Show More Cited By

Index Terms

Movie recommender system for profit maximization
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence

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Published In

cover image ACM Conferences

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

Copyright © 2013 ACM.

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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2013

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RecSys '13

Sponsor:

SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

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RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Fu YXu XLiu HYu QDai HQuek T(2024)Joint Assortment and Cache Planning for Practical User Choice Model in Wireless Content Caching NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2023.3297987(1-13)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3297987
De Biasio AJannach DNavarin N(2024)Model-based approaches to profit-aware recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123642249:PBOnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123642
Jannach DAbdollahpouri H(2023)A survey on multi-objective recommender systemsFrontiers in Big Data10.3389/fdata.2023.11578996Online publication date: 22-Mar-2023
https://doi.org/10.3389/fdata.2023.1157899
Fu YZhang YWong AQuek T(2023)Revenue Maximization: The Interplay Between Personalized Bundle Recommendation and Wireless Content CachingIEEE Transactions on Mobile Computing10.1109/TMC.2022.314280922:7(4253-4265)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TMC.2022.3142809
Sooklal SHosein P(2023)Optimization of Long-Term Profit when Cross-Selling Insurance Policies2023 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD)10.1109/ICTMOD59086.2023.10438158(1-6)Online publication date: 22-Nov-2023
https://doi.org/10.1109/ICTMOD59086.2023.10438158
Giahi RMaragheh RFarrokhsiar NXu JCho JKorpeoglu EKumar SAchan K(2023)GNN-GMVO: Graph Neural Networks for Optimizing Gross Merchandise Value in Similar Item Recommendation2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00189(1484-1492)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00189
Fletcher AOrmosi PSavani R(2023)Recommender Systems and Supplier Competition on PlatformsJournal of Competition Law & Economics10.1093/joclec/nhad00919:3(397-426)Online publication date: 19-Sep-2023
https://doi.org/10.1093/joclec/nhad009
Riofrío JRecalde LNavarrete R(2023)Profit vs Accuracy: Balancing the Impact on Users Introduced by Profit-Aware Recommender SystemsInformation and Communication Technologies10.1007/978-3-031-45438-7_12(175-192)Online publication date: 6-Oct-2023
https://doi.org/10.1007/978-3-031-45438-7_12
Guo LFlathmann CAnaraky RMcNeese NKnijnenburg BBellogín ABoratto LCena F(2022)The Effect of Recommendation Source and Justification on Professional Development Recommendations for High School TeachersProceedings of the 33rd ACM Conference on Hypertext and Social Media10.1145/3511095.3531280(175-185)Online publication date: 28-Jun-2022
https://dl.acm.org/doi/10.1145/3511095.3531280
Kompan MGaspar PMacina JCimerman MBielikova M(2022)Exploring Customer Price Preference and Product Profit Role in Recommender SystemsIEEE Intelligent Systems10.1109/MIS.2021.309276837:1(89-98)Online publication date: 1-Jan-2022
https://doi.org/10.1109/MIS.2021.3092768
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