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Analysis of recommendation algorithms for e-commerce

Authors:

Badrul Sarwar,

George Karypis,

Joseph Konstan,

John RiedlAuthors Info & Claims

EC '00: Proceedings of the 2nd ACM conference on Electronic commerce

Pages 158 - 167

https://doi.org/10.1145/352871.352887

Published: 17 October 2000 Publication History

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Cited By

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Junxi YWang ZChen C(2024)GCN-MF: A graph convolutional network based on matrix factorization for recommendationInnovation & Technology Advances10.61187/ita.v2i1.302:1(14-26)Online publication date: 8-Apr-2024
https://doi.org/10.61187/ita.v2i1.30
Guo YZhai THuang HWang L(2024)Problem exposure and problem solving: The impact of regulatory regimes on citizens' trust in regulated sectorsRegulation & Governance10.1111/rego.12610Online publication date: 5-Jul-2024
https://doi.org/10.1111/rego.12610
Mouhiha MOualhaj OMabrouk A(2024)Combining Collaborative Filtering and Content Based Filtering for Recommendation Systems2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM)10.1109/WINCOM62286.2024.10658169(1-6)Online publication date: 23-Jul-2024
https://doi.org/10.1109/WINCOM62286.2024.10658169
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Index Terms

Analysis of recommendation algorithms for e-commerce

Recommendations

Item-based top-N recommendation algorithms

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a certain user. ...
Comparison of group recommendation algorithms

In recent years recommender systems have become the common tool to handle the information overload problem of educational and informative web sites, content delivery systems, and online shops. Although most recommender systems make suggestions for ...
Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts

Many e-commerce sites present additional item recommendations to their visitors while they navigate the site, and ample evidence exists that such recommendations are valuable for both customers and providers. Academic research often focuses on the ...

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Published In

EC '00: Proceedings of the 2nd ACM conference on Electronic commerce

October 2000

271 pages

ISBN:1581132727

DOI:10.1145/352871

Chairmen:
Anant Jhingran
IBM
,
Jeff MacKie Mason
Univ. of Michigan
,
Doug Tygar
Univ. of California, Berkeley

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

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Published: 17 October 2000

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EC00: The 2nd ACM Conference on Electronic Commerce

October 17 - 20, 2000

Minnesota, Minneapolis, USA

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EC '00 Paper Acceptance Rate 29 of 150 submissions, 19%;

Overall Acceptance Rate 664 of 2,389 submissions, 28%

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Junxi YWang ZChen C(2024)GCN-MF: A graph convolutional network based on matrix factorization for recommendationInnovation & Technology Advances10.61187/ita.v2i1.302:1(14-26)Online publication date: 8-Apr-2024
https://doi.org/10.61187/ita.v2i1.30
Guo YZhai THuang HWang L(2024)Problem exposure and problem solving: The impact of regulatory regimes on citizens' trust in regulated sectorsRegulation & Governance10.1111/rego.12610Online publication date: 5-Jul-2024
https://doi.org/10.1111/rego.12610
Mouhiha MOualhaj OMabrouk A(2024)Combining Collaborative Filtering and Content Based Filtering for Recommendation Systems2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM)10.1109/WINCOM62286.2024.10658169(1-6)Online publication date: 23-Jul-2024
https://doi.org/10.1109/WINCOM62286.2024.10658169
Sun PWu LZhang KChen XWang M(2024)Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative FilteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3317068(1-14)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3317068
B GBalamurugan KBalamurugan ABalamurugan A(2024)Enhancing Recommender Systems with Hidden User Interests and Machine Learning : A Study of Multi -Label Classifiers2024 3rd International Conference for Innovation in Technology (INOCON)10.1109/INOCON60754.2024.10512060(1-5)Online publication date: 1-Mar-2024
https://doi.org/10.1109/INOCON60754.2024.10512060
Mangat BShah SKumbhar VSingh T(2024)Neural Network-Based Hybrid Recommendation System2024 5th International Conference for Emerging Technology (INCET)10.1109/INCET61516.2024.10593547(1-8)Online publication date: 24-May-2024
https://doi.org/10.1109/INCET61516.2024.10593547
Bougnol MDulà J(2024)The recommender problem with convex hullsINFOR: Information Systems and Operational Research10.1080/03155986.2024.230390662:2(259-272)Online publication date: 5-Feb-2024
https://doi.org/10.1080/03155986.2024.2303906
Xu MSun CZhan YLiu Y(2024)Impact and prediction of pollutant on mangrove and carbon stocks: A machine learning study based on urban remote sensing dataGeoscience Frontiers10.1016/j.gsf.2023.10166515:3(101665)Online publication date: May-2024
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Spreitzenbarth JBode CStuckenschmidt H(2024)Designing an AI purchasing requisition bundling generatorComputers in Industry10.1016/j.compind.2023.104043155:COnline publication date: 1-Feb-2024
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Gharibi SBagheriFard KParvin HNejatian SYaghoubyan S(2024)Ontology-based recommender system: a deep learning approachThe Journal of Supercomputing10.1007/s11227-023-05874-0Online publication date: 7-Feb-2024
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Recommendations

Item-based top-N recommendation algorithms

Comparison of group recommendation algorithms

Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts

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Recommendations

Item-based top-N recommendation algorithms

Comparison of group recommendation algorithms

Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts

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