research-article

Discount Sensitive Recommender System for Retail Business

Authors:

Hidetaka Izumo,

Takashi SonodaAuthors Info & Claims

EMPIRE '15: Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems 2015

Pages 33 - 40

https://doi.org/10.1145/2809643.2809646

Published: 16 September 2015 Publication History

Abstract

User preferences for items are not the only determinant of purchase. Price promotion influences user's buying habits and changes items that are put in a basket. Such a reaction to discount depends on the user personality. In this paper, we propose a recommendation algorithm with personalized discount sensitivity. The effectiveness of the proposed model was verified using a public retail dataset. Correlation between personal persistence in repeat purchases and discount sensitivity of users was also investigated.

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Cited By

Poppink BFrasincar FRobal T(2023)An experimental study on re-ranking web shop search results using semantic segmentation of user profilesElectronic Commerce Research and Applications10.1016/j.elerap.2023.10131062:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.elerap.2023.101310
Song HKim Y(2020)A dog food recommendation system based on nutrient suitabilityExpert Systems10.1111/exsy.1262338:2Online publication date: 7-Aug-2020
https://doi.org/10.1111/exsy.12623
Sato MSingh JTakemori SSonoda TZhang QOhkuma TBogers TSaid ABrusilovsky PTikk D(2019)Uplift-based evaluation and optimization of recommendersProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347018(296-304)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347018
Show More Cited By

Index Terms

Discount Sensitive Recommender System for Retail Business
1. Information systems
  1. Information retrieval

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cover image ACM Other conferences

EMPIRE '15: Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems 2015

September 2015

45 pages

ISBN:9781450336154

DOI:10.1145/2809643

Copyright © 2015 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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University of Ljubljana: University of Ljubljana
Johannes Kepler University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2015

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Conference

EMPIRE '15

EMPIRE '15: 3rd Workshop on Emotions and Personality in Personalized Systems 2015

September 16 - 20, 2015

Vienna, Austria

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EMPIRE '15 Paper Acceptance Rate 6 of 9 submissions, 67%;

Overall Acceptance Rate 6 of 9 submissions, 67%

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Cited By

Poppink BFrasincar FRobal T(2023)An experimental study on re-ranking web shop search results using semantic segmentation of user profilesElectronic Commerce Research and Applications10.1016/j.elerap.2023.10131062:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.elerap.2023.101310
Song HKim Y(2020)A dog food recommendation system based on nutrient suitabilityExpert Systems10.1111/exsy.1262338:2Online publication date: 7-Aug-2020
https://doi.org/10.1111/exsy.12623
Sato MSingh JTakemori SSonoda TZhang QOhkuma TBogers TSaid ABrusilovsky PTikk D(2019)Uplift-based evaluation and optimization of recommendersProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347018(296-304)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347018
Sato MKawai SNobuhara H(2019)Action-Triggering Recommenders: Uplift Optimization and Persuasive Explanation2019 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2019.00155(1060-1069)Online publication date: Nov-2019
https://doi.org/10.1109/ICDMW.2019.00155
Colombo-Mendoza LParedes-Valverde MSalas-Zárate MBustos-López MSánchez-Cervantes JAlor-Hernández G(2019)Recommender Systems in the Offline Retailing Domain: A Systematic Literature ReviewTechniques, Tools and Methodologies Applied to Global Supply Chain Ecosystems10.1007/978-3-030-26488-8_17(383-409)Online publication date: 30-Aug-2019
https://doi.org/10.1007/978-3-030-26488-8_17

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