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Recommender systems in e-commerce

Authors:

J. Ben Schafer,

Joseph Konstan,

John RiedlAuthors Info & Claims

EC '99: Proceedings of the 1st ACM conference on Electronic commerce

Pages 158 - 166

https://doi.org/10.1145/336992.337035

Published: 01 November 1999 Publication History

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Lee MChoi E(2024)A Study on the Impact of Quality and HCI Factors of a Deep Learning-Based Makeup Recommendation System on Acceptance Intention: Focusing on the Mediating Effects of Users’ Perceived Trust and ImmersionJournal of the Korean Society of Cosmetology10.52660/JKSC.2024.30.5.96030:5(960-970)Online publication date: 31-Oct-2024
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Tanveer Ahmad Lone Dr. Ajit Kumar Dr. Muzafar Rasool Bhat (2024)Exploring the Efficiency of Hybrid Recommender Systems Implemented with TensorFlow FrameworkInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-19979(528-533)Online publication date: 29-Oct-2024
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Index Terms

Recommender systems in e-commerce
1. Applied computing
  1. Electronic commerce
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms

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A Graph Based Approach to Extract a Neighborhood Customer Community for Collaborative Filtering
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Published In

EC '99: Proceedings of the 1st ACM conference on Electronic commerce

November 1999

187 pages

ISBN:1581131763

DOI:10.1145/336992

Chairmen:
Stuart Feldman
IBM
,
Michael Wellman
Univ. of Michigan, Ann Arbor

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

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Publication History

Published: 01 November 1999

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EC99

Sponsor:

SIGecom

EC99: ACM Conference on Electronic Commerce

November 3 - 5, 1999

Colorado, Denver, USA

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

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Lee MChoi E(2024)A Study on the Impact of Quality and HCI Factors of a Deep Learning-Based Makeup Recommendation System on Acceptance Intention: Focusing on the Mediating Effects of Users’ Perceived Trust and ImmersionJournal of the Korean Society of Cosmetology10.52660/JKSC.2024.30.5.96030:5(960-970)Online publication date: 31-Oct-2024
https://doi.org/10.52660/JKSC.2024.30.5.960
Tanveer Ahmad Lone Dr. Ajit Kumar Dr. Muzafar Rasool Bhat (2024)Exploring the Efficiency of Hybrid Recommender Systems Implemented with TensorFlow FrameworkInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-19979(528-533)Online publication date: 29-Oct-2024
https://doi.org/10.48175/IJARSCT-19979
Abbas WZhang ZAsim MChen JAhmad S(2024)AI-Driven Precision Clothing Classification: Revolutionizing Online Fashion Retailing with Hybrid Two-Objective LearningInformation10.3390/info1504019615:4(196)Online publication date: 2-Apr-2024
https://doi.org/10.3390/info15040196
Shrestha DWenan TShrestha DRajkarnikar NJeong S(2024)Personalized Tourist Recommender System: A Data-Driven and Machine-Learning ApproachComputation10.3390/computation1203005912:3(59)Online publication date: 18-Mar-2024
https://doi.org/10.3390/computation12030059
Park MOh J(2024)Enhancing E-Commerce Recommendation Systems with Multiple Item Purchase Data: A Bidirectional Encoder Representations from Transformers-Based ApproachApplied Sciences10.3390/app1416725514:16(7255)Online publication date: 17-Aug-2024
https://doi.org/10.3390/app14167255
Iana AAlam MPaulheim H(2024)A survey on knowledge-aware news recommender systemsSemantic Web10.3233/SW-22299115:1(21-82)Online publication date: 12-Jan-2024
https://doi.org/10.3233/SW-222991
Niederhaus MMigenda NWeller JSchenck WKohlhase M(2024)Technical Readiness of Prescriptive Analytics Platforms: A Survey2024 35th Conference of Open Innovations Association (FRUCT)10.23919/FRUCT61870.2024.10516367(509-519)Online publication date: 24-Apr-2024
https://doi.org/10.23919/FRUCT61870.2024.10516367
Sagi MSaldanha PShani GMoskovitch R(2024)Pro-cycling team cyclist assignment for an upcoming racePLOS ONE10.1371/journal.pone.029727019:3(e0297270)Online publication date: 4-Mar-2024
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Fahrudin TWijaya D(2024)New custom rating for improving recommendation system performanceJournal of Big Data10.1186/s40537-024-00952-311:1Online publication date: 2-Jul-2024
https://doi.org/10.1186/s40537-024-00952-3
Almashjari SIsmail H(2024)Intelligent Food Recommendation Framework Based on Social Media Behavioral DataProceedings of the Cognitive Models and Artificial Intelligence Conference10.1145/3660853.3660883(124-129)Online publication date: 25-May-2024
https://dl.acm.org/doi/10.1145/3660853.3660883
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Recommendations

E-Commerce Recommendation Applications

A Graph Based Approach to Extract a Neighborhood Customer Community for Collaborative Filtering

Improving the performance of recommender system by exploiting the categories of products