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A sentiment-based item description approach for kNN collaborative filtering

Published: 13 April 2015 Publication History

Abstract

In this paper, we propose an approach based on sentiment analysis to describe items in a neighborhood-based collaborative filtering model. We use unstructured users' reviews to produce a vector-based representation that considers the overall sentiment of those reviews towards specific features. We propose and compare two different techniques to obtain and score such features from textual content, namely term-based and aspect-based feature extraction. Finally, our proposal is compared against structured metadata under the same recommendation algorithm, whose results show a significant improvement over the baselines.

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  • (2024)A Comparative Study of Sentiment-Aware Collaborative Filtering Algorithms for Arabic Recommendation SystemsIEEE Access10.1109/ACCESS.2024.348965812(174441-174454)Online publication date: 2024
  • (2023)Metadata Based Cross-Domain Recommender Framework Using Neighborhood Mapping2023 International Conference on Sustainable Technology and Engineering (i-COSTE)10.1109/i-COSTE60462.2023.10500780(1-8)Online publication date: 4-Dec-2023
  • (2023)Metadata based Cross-Domain Recommender Framework using Neighborhood Mapping2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE59766.2023.10487686(1-8)Online publication date: 4-Dec-2023
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cover image ACM Conferences
SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
April 2015
2418 pages
ISBN:9781450331968
DOI:10.1145/2695664
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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Publication History

Published: 13 April 2015

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Author Tags

  1. algorithm
  2. collaborative filtering
  3. item representation
  4. recommender systems
  5. sentiment analysis

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  • Research-article

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SAC 2015
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SAC 2015: Symposium on Applied Computing
April 13 - 17, 2015
Salamanca, Spain

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SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
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Cited By

View all
  • (2024)A Comparative Study of Sentiment-Aware Collaborative Filtering Algorithms for Arabic Recommendation SystemsIEEE Access10.1109/ACCESS.2024.348965812(174441-174454)Online publication date: 2024
  • (2023)Metadata Based Cross-Domain Recommender Framework Using Neighborhood Mapping2023 International Conference on Sustainable Technology and Engineering (i-COSTE)10.1109/i-COSTE60462.2023.10500780(1-8)Online publication date: 4-Dec-2023
  • (2023)Metadata based Cross-Domain Recommender Framework using Neighborhood Mapping2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)10.1109/CSDE59766.2023.10487686(1-8)Online publication date: 4-Dec-2023
  • (2022)Hybrid Recommender System Using Emotional Fingerprints ModelResearch Anthology on Implementing Sentiment Analysis Across Multiple Disciplines10.4018/978-1-6684-6303-1.ch056(1076-1100)Online publication date: 10-Jun-2022
  • (2021)Multi Criteria Decisions—A Modernistic Approach to Designing Recommender SystemsIntelligent Computing Paradigm and Cutting-edge Technologies10.1007/978-3-030-65407-8_20(231-243)Online publication date: 22-Apr-2021
  • (2020)A Sentiment-based Similarity Model for Recommendation Systems2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)10.1109/SYNASC51798.2020.00044(224-230)Online publication date: Sep-2020
  • (2019)Hybrid Recommender System Using Emotional Fingerprints ModelInternational Journal of Information Retrieval Research10.4018/IJIRR.20190701049:3(48-70)Online publication date: Jul-2019
  • (2019)Multi-Criteria Review-Based Recommender System–The State of the ArtIEEE Access10.1109/ACCESS.2019.29548617(169446-169468)Online publication date: 2019
  • (2018)Enhancing Multi-Aspect Collaborative Filtering for Personalized Recommendation2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP)10.1109/INFRKM.2018.8464760(1-6)Online publication date: Mar-2018
  • (2017)Exploiting feature extraction techniques on users’ reviews for movies recommendationJournal of the Brazilian Computer Society10.1186/s13173-017-0057-823:1Online publication date: 5-Jun-2017
  • Show More Cited By

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