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Ensemble Learning in Recommender Systems: Combining Multiple User Interactions for Ranking Personalization

Authors:

Arthur da Costa Fortes,

Marcelo Garcia ManzatoAuthors Info & Claims

WebMedia '14: Proceedings of the 20th Brazilian Symposium on Multimedia and the Web

Pages 47 - 54

https://doi.org/10.1145/2664551.2664556

Published: 18 November 2014 Publication History

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Abstract

In this paper, we propose a technique that uses multimodal interactions of users to generate a more accurate list of recommendations optimized for the user . Our approach is a response to the actual scenario on the Web which allows users to interact with the content in different ways, and thus, more information about his preferences can be obtained to improve recommendation. The proposal consists of an ensemble learning technique that combines rankings generated by unimodal recommenders based on particular interaction types. By using a combination of different types of feedback from users, we are able to provide better recommendations, as shown by our experimental evaluation.

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Zhang PMa RZhang F(2024)An Ensemble Approach for Bundle Recommendation Based on Performance Evaluation2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT)10.1109/ICCECT60629.2024.10546145(127-132)Online publication date: 26-Apr-2024
https://doi.org/10.1109/ICCECT60629.2024.10546145
Vente T(2023)Advancing Automation of Design Decisions in Recommender System PipelinesProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608886(1355-1360)Online publication date: 14-Sep-2023
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Zanon ASouza LPressato DManzato M(2022)A user study with aspect‐based sentiment analysis for similarity of items in content‐based recommendationsExpert Systems10.1111/exsy.1299139:8Online publication date: 14-Mar-2022
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Index Terms

Ensemble Learning in Recommender Systems: Combining Multiple User Interactions for Ranking Personalization
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Document representation

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Published In

WebMedia '14: Proceedings of the 20th Brazilian Symposium on Multimedia and the Web

November 2014

256 pages

ISBN:9781450332309

DOI:10.1145/2664551

General Chair:
Raoni Kulesza
UFPB - Universidade Federal da Paraíba
,
Program Chair:
Tatiana Tavares
UFPB - Universidade Federal da Paraíba

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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Published: 18 November 2014

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November 18 - 21, 2014

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Overall Acceptance Rate 270 of 873 submissions, 31%

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Zhang PMa RZhang F(2024)An Ensemble Approach for Bundle Recommendation Based on Performance Evaluation2024 IEEE 2nd International Conference on Control, Electronics and Computer Technology (ICCECT)10.1109/ICCECT60629.2024.10546145(127-132)Online publication date: 26-Apr-2024
https://doi.org/10.1109/ICCECT60629.2024.10546145
Vente T(2023)Advancing Automation of Design Decisions in Recommender System PipelinesProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608886(1355-1360)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608886
Zanon ASouza LPressato DManzato M(2022)A user study with aspect‐based sentiment analysis for similarity of items in content‐based recommendationsExpert Systems10.1111/exsy.1299139:8Online publication date: 14-Mar-2022
https://doi.org/10.1111/exsy.12991
Gupta GKatarya R(2021)EnPSO: An AutoML Technique for Generating Ensemble Recommender SystemArabian Journal for Science and Engineering10.1007/s13369-021-05670-z46:9(8677-8695)Online publication date: 23-Apr-2021
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Zanon ASouza LPressato DManzato Mde Salles Soares Neto C(2020)WordRecommenderProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3428658.3431093(181-184)Online publication date: 30-Nov-2020
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Skopal TGurrin CÞór Jónsson BKando NSchoeffmann KChen PO'Connor N(2020)On Visualizations in the Role of Universal Data RepresentationProceedings of the 2020 International Conference on Multimedia Retrieval10.1145/3372278.3390743(362-367)Online publication date: 8-Jun-2020
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D’Addio RMarinho RManzato M(2019)Combining different metadata views for better recommendation accuracyInformation Systems10.1016/j.is.2019.01.00883:C(1-12)Online publication date: 1-Jul-2019
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Monteiro Fdos Santos Tde Freitas Bulcão-Neto RMacedo ANeto MNovais RFerraz CViana W(2018)An Answer Ranking Method in Medical Social NetworksProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243099(355-362)Online publication date: 16-Oct-2018
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Investigating serendipity in recommender systems based on real user feedback

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Acquiring User Information Needs for Recommender Systems

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Investigating serendipity in recommender systems based on real user feedback

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