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A case study in a recommender system based on purchase data

Authors:

Savaneary Sean,

Julien Delporte,

Sébastien Guérif,

Céline Rouveirol,

Nicolas Usunier,

Françoise Fogelman-Soulié,

Frédéric Dufau-JoelAuthors Info & Claims

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 377 - 385

https://doi.org/10.1145/2020408.2020470

Published: 21 August 2011 Publication History

Abstract

Collaborative filtering has been extensively studied in the context of ratings prediction. However, industrial recommender systems often aim at predicting a few items of immediate interest to the user, typically products that (s)he is likely to buy in the near future. In a collaborative filtering setting, the prediction may be based on the user's purchase history rather than rating information, which may be unreliable or unavailable. In this paper, we present an experimental evaluation of various collaborative filtering algorithms on a real-world dataset of purchase history from customers in a store of a French home improvement and building supplies chain. These experiments are part of the development of a prototype recommender system for salespeople in the store. We show how different settings for training and applying the models, as well as the introduction of domain knowledge may dramatically influence both the absolute and the relative performances of the different algorithms. To the best of our knowledge, the influence of these parameters on the quality of the predictions of recommender systems has rarely been reported in the literature.

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Cited By

Apostolopoulos PWang ZWang HXu TZhou CVirochsiri KZhou NMarkov I(2024)Personalization for web-based services using offline reinforcement learningMachine Language10.1007/s10994-024-06525-y113:5(3049-3071)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10994-024-06525-y
He LLuo KDing ZShao HBai B(2024)A Reinforcement Learning Approach for Personalized Diversity in Feeds RecommendationArtificial Intelligence10.1007/978-981-99-9119-8_42(463-475)Online publication date: 3-Feb-2024
https://doi.org/10.1007/978-981-99-9119-8_42
Godinot ATarissan F(2023)Measuring the effect of collaborative filtering on the diversity of users’ attentionApplied Network Science10.1007/s41109-022-00530-78:1Online publication date: 25-Jan-2023
https://doi.org/10.1007/s41109-022-00530-7
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Index Terms

A case study in a recommender system based on purchase data
1. Information systems

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Published In

cover image ACM Conferences

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2011

1446 pages

ISBN:9781450308137

DOI:10.1145/2020408

General Chair:
Chid Apte
IBM Research
,
Program Chairs:
Joydeep Ghosh
UT Austin
,
Padhraic Smyth
UC Irvine

Copyright © 2011 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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Publication History

Published: 21 August 2011

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KDD '11: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2011

California, San Diego, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Apostolopoulos PWang ZWang HXu TZhou CVirochsiri KZhou NMarkov I(2024)Personalization for web-based services using offline reinforcement learningMachine Language10.1007/s10994-024-06525-y113:5(3049-3071)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10994-024-06525-y
He LLuo KDing ZShao HBai B(2024)A Reinforcement Learning Approach for Personalized Diversity in Feeds RecommendationArtificial Intelligence10.1007/978-981-99-9119-8_42(463-475)Online publication date: 3-Feb-2024
https://doi.org/10.1007/978-981-99-9119-8_42
Godinot ATarissan F(2023)Measuring the effect of collaborative filtering on the diversity of users’ attentionApplied Network Science10.1007/s41109-022-00530-78:1Online publication date: 25-Jan-2023
https://doi.org/10.1007/s41109-022-00530-7
Sysko-Romańczuk SZaborek PWróblewska ADąbrowski JTkachuk S(2022)Data modalities, consumer attributes and recommendation performance in the fashion industryElectronic Markets10.1007/s12525-022-00579-332:3(1279-1292)Online publication date: 11-Aug-2022
https://doi.org/10.1007/s12525-022-00579-3
Mizuno KNaito KEhara M(2022)The Evaluation of the Angled Antenna Based Direction Estimation Scheme for RFID TagsMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-030-94822-1_49(754-768)Online publication date: 8-Feb-2022
https://doi.org/10.1007/978-3-030-94822-1_49
Martínez RCarrasco RSanchez-Figueroa CGavilan D(2021)An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail BusinessMathematics10.3390/math91618369:16(1836)Online publication date: 4-Aug-2021
https://doi.org/10.3390/math9161836
Bouarour NBenouaret IAmer-Yahia S(2021)How Useful is Meta-Recommendation? An Empirical Investigation2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671808(600-606)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671808
Barkan OFuchs YCaciularu AKoenigstein N(2020)Explainable Recommendations via Attentive Multi-Persona Collaborative FilteringProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412226(468-473)Online publication date: 22-Sep-2020
https://dl.acm.org/doi/10.1145/3383313.3412226
Faggioli GPolato MAiolli F(2020)Recency Aware Collaborative Filtering for Next Basket RecommendationProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3394850(80-87)Online publication date: 7-Jul-2020
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Benouaret IAmer-Yahia S(2020)A Comparative Evaluation of Top-N Recommendation Algorithms: Case Study with Total Customers2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378404(4499-4508)Online publication date: 10-Dec-2020
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