A user study with aspect‐based sentiment analysis for similarity of items in content‐based recommendations-Reference-Cited by-同舟云学术

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A user study with aspect‐based sentiment analysis for similarity of items in content‐based recommendations

Published:2022-03-14 Issue:8 Volume:39 Page:
ISSN:0266-4720
Container-title:Expert Systems
language:en
Short-container-title:Expert Systems

Author:

Zanon André Levi¹^ORCID,Souza Luan¹^ORCID,Pressato Diany¹^ORCID,Manzato Marcelo Garcia¹^ORCID

Affiliation:

1. Institute of Mathematics and Computer Sciences University of Sao Paulo Sao Paulo Brazil

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12991

Reference33 articles.

1. Recommender Systems

2. Bauman K. Liu B. &Tuzhilin A.(2017).Aspect based recommendations: Recommending items with the most valuable aspects based on user reviews. InProceedings of the 23rd acm sigkdd international conference on knowledge discovery and datamining(p.717–725).Association for Computing Machinery. doi:https://doi.org/10.1145/3097983.3098170

3. Bellini V. Schiavone A. Di Noia T. Ragone A. &Di Sciascio E.(2018).Knowledge‐aware autoencoders for explainable recommender systems.Proceedings of the 3rd workshop on deep learning for recommender systems(pp.24–31).

4. Chang S. Harper F. M. &Terveen L. G.(2016).Crowd‐based personalized natural language explanations for recommendations.Proceedings of the 10th acm conference on recommender systems(p.175–182).Association for Computing Machinery.https://doi.org/10.1145/2959100.2959153

5. Recommender systems based on user reviews: the state of the art

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