research-article

User Preferences Analysis Using Visual Stimuli

Author:

Peter GasparAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 436 - 440

https://doi.org/10.1145/3109859.3120955

Published: 27 August 2017 Publication History

Abstract

Recommender systems aim at enhancing user experience on the Web by employing the results of users behavior analysis for recommending items. However, user behavior is usually influenced by various aspects. Even though visual stimuli greatly influence almost every part of our life, it is yet poorly reflected in the domain of recommendation. In our work, we study the impact of visual stimuli (specifically images) on recommendation process on the Web. We focus on the domains where the impact of images is substantial (e.g.,~movies and shopping). First results of our experiments suggest that features extracted from the images are able to improve the ranking of the current recommendation approaches.

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Cited By

Zhang QLu JJin Y(2020)Artificial intelligence in recommender systemsComplex & Intelligent Systems10.1007/s40747-020-00212-w7:1(439-457)Online publication date: 1-Nov-2020
https://doi.org/10.1007/s40747-020-00212-w
Yin RLi KLu JZhang G(2019)Enhancing Fashion Recommendation with Visual Compatibility RelationshipThe World Wide Web Conference10.1145/3308558.3313739(3434-3440)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313739

Index Terms

User Preferences Analysis Using Visual Stimuli
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Learning to rank
        Ranking
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 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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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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Scientific Grant Agency of the Slovak Republic
Slovak Research and Development Agency

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RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

Acceptance Rates

RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Zhang QLu JJin Y(2020)Artificial intelligence in recommender systemsComplex & Intelligent Systems10.1007/s40747-020-00212-w7:1(439-457)Online publication date: 1-Nov-2020
https://doi.org/10.1007/s40747-020-00212-w
Yin RLi KLu JZhang G(2019)Enhancing Fashion Recommendation with Visual Compatibility RelationshipThe World Wide Web Conference10.1145/3308558.3313739(3434-3440)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313739

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