Multimodal data fusion framework based on autoencoders for top-N recommender systems-Reference-Cited by-同舟云学术

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Multimodal data fusion framework based on autoencoders for top-N recommender systems

Published:2019-03-30 Issue:9 Volume:49 Page:3267-3282
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Conceiç ao Felipe L. A.,Pádua Flávio L. C.,Lacerda Anisio,Machado Adriano C.,Dalip Daniel H.^ORCID

Funder

Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico

Funda??o de Amparo ? Pesquisa do Estado de Minas Gerais

Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior

Centro Federal de Educa??o Tecnol?gica de Minas Gerais

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

http://link.springer.com/content/pdf/10.1007/s10489-019-01430-7.pdf

Reference57 articles.

1. Ahmed M, Imtiaz MT, Khan R (2018) Movie recommendation system using clustering and pattern recognition network. In: 2018 IEEE 8th annual computing and communication workshop and conference (CCWC). IEEE, pp 143–147

2. Baeza-Yates R, Ribeiro-Neto B et al (1999) Modern information retrieval, vol 463. ACM Press, New York

3. Beel J, Gipp B, Langer S, Breitinger C (2015) Research-paper recommender systems: a literature survey. International Journal on Digital Libraries, pp 1–34. https://doi.org/10.1007/s00799-015-0156-0

4. Beutel A, Covington P, Jain S, Xu C, Li J, Gatto V, Chi EH (2018) Latent cross: Making use of context in recurrent recommender systems. In: Proceedings of the eleventh ACM international conference on Web search and data mining. ACM, pp 46–54

5. Bobadilla J, Hernando A, Ortega F, Gutiérrez A (2012) Collaborative filtering based on significances. Inf Sci 185(1):1–17 . https://doi.org/10.1016/j.ins.2011.09.014 , http://www.scopus.com/inward/record.url?eid=2-s2.0-80755139565&partnerID=40&md5=ff16abb2e6d3731d4f4683d0f56018ae

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