The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
À medida que o mercado de conteúdos multimédia continua a sua rápida expansão, a quantidade de conteúdos de imagem utilizados em serviços de telefonia móvel, bibliotecas digitais e serviços de catálogo está a aumentar notavelmente. Apesar deste rápido crescimento, os utilizadores experimentam elevados níveis de frustração quando procuram a imagem desejada. Embora novas imagens sejam lucrativas para os provedores de serviços, os métodos tradicionais de filtragem colaborativa não podem recomendá-las. Para resolver este problema, neste artigo, propomos um método de filtragem colaborativa baseada em recursos (FBCF) para refletir a preferência mais recente do usuário, representando sua sequência de compra no espaço visual de recursos. A abordagem proposta representa as imagens que foram adquiridas no passado como clusters de recursos no espaço de recursos multidimensional e então seleciona vizinhos usando uma função de distância entre clusters entre seus clusters de recursos. Vários experimentos usando dados de imagens reais demonstram que a abordagem proposta fornece uma recomendação de maior qualidade e melhor desempenho do que técnicas típicas de filtragem colaborativa e de filtragem baseada em conteúdo.
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Deok-Hwan KIM, "Image Recommendation Algorithm Using Feature-Based Collaborative Filtering" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 3, pp. 413-421, March 2009, doi: 10.1587/transinf.E92.D.413.
Abstract: As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.413/_p
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@ARTICLE{e92-d_3_413,
author={Deok-Hwan KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Image Recommendation Algorithm Using Feature-Based Collaborative Filtering},
year={2009},
volume={E92-D},
number={3},
pages={413-421},
abstract={As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.},
keywords={},
doi={10.1587/transinf.E92.D.413},
ISSN={1745-1361},
month={March},}
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TY - JOUR
TI - Image Recommendation Algorithm Using Feature-Based Collaborative Filtering
T2 - IEICE TRANSACTIONS on Information
SP - 413
EP - 421
AU - Deok-Hwan KIM
PY - 2009
DO - 10.1587/transinf.E92.D.413
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2009
AB - As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.
ER -