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Maximizing Marginal Utility per Dollar for Economic Recommendation

Published: 13 May 2019 Publication History

Abstract

Understanding the economic nature of consumer decisions in e-Commerce is important to personalized recommendation systems. Established economic theories claim that informed consumers always attempt to maximize their utility by choosing the items of the largest marginal utility per dollar (MUD) within their budgets. For example, gaining 5 dollars of extra benefit by spending 10 dollars makes a consumer much more satisfied than having the same amount of extra benefit by spending 20 dollars, although the second product may have higher absolute utility value. Meanwhile, making purchases online may be risky decisions that could cause dissatisfaction. For example, people may give low ratings towards purchased items that they thought they would like when placing the order. Therefore, the design of recommender systems should also take users' risk attitudes into consideration to better learn consumer behaviors.
Motivated by the first consideration, in this paper, we propose a learning algorithm to maximize marginal utility per dollar for recommendations. With the second, economic theory shows that rational people can be arbitrarily close to risk neutral when stakes are arbitrarily small, and this is generally applicable to consumer online purchase behaviors because most people spend a small portion of their total wealth for a single purchase. To integrate this theory with machine learning, we propose to augment MUD optimization with approximate risk-neural constraint to generate personalized recommendations. Experiments on real-world e-Commerce datasets show that our approach is able to achieve better performance than many classical recommendation methods, in terms of both traditional recommendation measures such as precision and recall, as well as economic measures such as MUD.

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  • (2024)A Review-Level Sentiment Information Enhanced Multitask Learning Approach for Explainable RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.337672811:5(5925-5934)Online publication date: Oct-2024
  • (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
  • (2024)Economic recommender systems – a systematic reviewElectronic Commerce Research and Applications10.1016/j.elerap.2023.10135263:COnline publication date: 17-Apr-2024
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cover image ACM Other conferences
WWW '19: The World Wide Web Conference
May 2019
3620 pages
ISBN:9781450366748
DOI:10.1145/3308558
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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Publication History

Published: 13 May 2019

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Author Tags

  1. Computational Economics
  2. Marginal Utility per Dollar
  3. Personalization
  4. Recommendation Systems
  5. Risk Attitude

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WWW '19
WWW '19: The Web Conference
May 13 - 17, 2019
CA, San Francisco, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2024)A Review-Level Sentiment Information Enhanced Multitask Learning Approach for Explainable RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.337672811:5(5925-5934)Online publication date: Oct-2024
  • (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
  • (2024)Economic recommender systems – a systematic reviewElectronic Commerce Research and Applications10.1016/j.elerap.2023.10135263:COnline publication date: 17-Apr-2024
  • (2022)Explainable Fairness in RecommendationProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531973(681-691)Online publication date: 6-Jul-2022
  • (2022)AutoLossGen: Automatic Loss Function Generation for Recommender SystemsProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531941(1304-1315)Online publication date: 6-Jul-2022
  • (2021)CIKM 2021 Tutorial on Fairness of Machine Learning in Recommender SystemsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3483280(4857-4860)Online publication date: 26-Oct-2021
  • (2021)WSDM 2021 Tutorial on Conversational Recommendation SystemsProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441661(1134-1136)Online publication date: 8-Mar-2021
  • (2021)CSR 2021: The 1st International Workshop on Causality in Search and RecommendationProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462817(2677-2680)Online publication date: 11-Jul-2021
  • (2020)Understanding Echo Chambers in E-commerce Recommender SystemsProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401431(2261-2270)Online publication date: 25-Jul-2020

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