research-article

Recommender system based on click stream data using association rule mining

Authors:

Bong-Jin YumAuthors Info & Claims

Volume 38, Issue 10

Pages 13320 - 13327

https://doi.org/10.1016/j.eswa.2011.04.154

Published: 15 September 2011 Publication History

Graphical Abstract

Highlights

► We developed a recommender system based on navigational and behavioral pattern data. ► The proposed system calculates the confidence levels between clicked products using association rule mining. ► The preference level was estimated through the linear combination of the above three confidence levels. ► The results from the experimental study clearly showed that the proposed method is superior to the conventional methods.

Abstract

In the most studies of the past, only purchase data of users were used in e-commerce recommender system, while navigational and behavioral pattern data were not utilized. However, Kim, Yum, Song, and Kim (2005) developed a collaborative filtering technique based on navigational and behavioral patterns of customers in e-commerce sites. In this article, we improve on Kim et al. (2005) methods and further develop a novel recommender system. The proposed system calculates the confidence levels between clicked products, between the products placed in the basket, and between purchased products, respectively, and then the preference level was estimated through the linear combination of the above three confidence levels. To assess the effectiveness of the proposed approach, an empirical study was conducted by constructing an experimental e-commerce site for compact disc albums. The results from the experimental study clearly showed that the proposed method is superior to Kim et al. (2005) method.

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Cited By

Hikmawati ENugroho HSurendro K(2024)Improve the Quality of Recommender Systems based on Collaborative Filtering with Missing Data ImputationProceedings of the 2024 13th International Conference on Software and Computer Applications10.1145/3651781.3651793(75-80)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1145/3651781.3651793
Nasir MEzeife C(2023)A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product RecommendationSN Computer Science10.1007/s42979-023-02166-54:6Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1007/s42979-023-02166-5
Cai ZZhen YHe MChen LSun LZhou TDu Y(2022)Browsing Behavioral Intent Prediction on Product Recommendation Pages of E-commerce PlatformArtificial Intelligence10.1007/978-3-031-20500-2_3(33-45)Online publication date: 27-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-20500-2_3
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Index Terms

Recommender system based on click stream data using association rule mining
1. Applied computing
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 38, Issue 10

Sep 2011

1499 pages

ISSN:0957-4174

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 15 September 2011

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Cited By

Hikmawati ENugroho HSurendro K(2024)Improve the Quality of Recommender Systems based on Collaborative Filtering with Missing Data ImputationProceedings of the 2024 13th International Conference on Software and Computer Applications10.1145/3651781.3651793(75-80)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1145/3651781.3651793
Nasir MEzeife C(2023)A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product RecommendationSN Computer Science10.1007/s42979-023-02166-54:6Online publication date: 15-Sep-2023
https://dl.acm.org/doi/10.1007/s42979-023-02166-5
Cai ZZhen YHe MChen LSun LZhou TDu Y(2022)Browsing Behavioral Intent Prediction on Product Recommendation Pages of E-commerce PlatformArtificial Intelligence10.1007/978-3-031-20500-2_3(33-45)Online publication date: 27-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-20500-2_3
Hikmawati EUlfa Maulidevi NSurendro K(2021)Multi-criteria recommender system model for lockdown decision of Covid-19Proceedings of the 2021 10th International Conference on Software and Computer Applications10.1145/3457784.3457790(39-44)Online publication date: 23-Feb-2021
https://dl.acm.org/doi/10.1145/3457784.3457790
Dang DChen CYu WHu H(2020)A semantic-aware collaborative filtering recommendation method for emergency plans in response to meteorological hazardsIntelligent Data Analysis10.3233/IDA-19457124:3(705-721)Online publication date: 21-May-2020
https://dl.acm.org/doi/10.3233/IDA-194571
Badidi EAtif YSheng QMaheswaran M(2019)On personalized cloud service provisioning for mobile users using adaptive and context-aware service compositionComputing10.1007/s00607-018-0631-8101:4(291-318)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s00607-018-0631-8
Xiao YEzeife C(2018)E-Commerce Product Recommendation Using Historical Purchases and Clickstream DataBig Data Analytics and Knowledge Discovery10.1007/978-3-319-98539-8_6(70-82)Online publication date: 3-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-319-98539-8_6
Utku AAydogan EMutlu BAkcayol M(2017)A New Recommender System Based on Multiple Parameters and Extended User Behavior AnalysisProceedings of the 9th International Conference on Information Management and Engineering10.1145/3149572.3149594(133-138)Online publication date: 9-Oct-2017
https://dl.acm.org/doi/10.1145/3149572.3149594
Villanueva DGonzález-Carrasco ILópez-Cuadrado JLado N(2016)SMOREScience of Computer Programming10.1016/j.scico.2015.06.008121:C(16-33)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.scico.2015.06.008
Iwanaga JNishimura NSukegawa NTakano Y(2016)Estimating product-choice probabilities from recency and frequency of page viewsKnowledge-Based Systems10.1016/j.knosys.2016.02.00699:C(157-167)Online publication date: 1-May-2016
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