Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

Attribute-aware CF models aim at rating prediction given not only the historical rating given by users to items but also the information associated with users (e.g., age), items (e.g., price), and ratings (e.g., rating time). This paper surveys work in the past decade to develop attribute-aware CF s...

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Main Authors:	Wen-Hao Chen, Chin-Chi Hsu, Yi-An Lai, Vincent Liu, Mi-Yen Yeh, Shou-De Lin
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2020-01-01
Series:	Frontiers in Big Data
Subjects:	recommender system matrix factorization collaborative filtering attribute survey
Online Access:	https://www.frontiersin.org/article/10.3389/fdata.2019.00049/full

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spelling	doaj-78e1a3f1daff420fbe7cabe1ddcfa0692020-11-25T02:21:54ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2020-01-01210.3389/fdata.2019.00049490677Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and ClassificationWen-Hao Chen0Chin-Chi Hsu1Yi-An Lai2Vincent Liu3Mi-Yen Yeh4Shou-De Lin5Department of Computer Science and Information Engineering, National Taiwan University, Taipei, TaiwanInstitute of Information Science, Academia Sinica, Taipei, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan University, Taipei, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan University, Taipei, TaiwanInstitute of Information Science, Academia Sinica, Taipei, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan University, Taipei, TaiwanAttribute-aware CF models aim at rating prediction given not only the historical rating given by users to items but also the information associated with users (e.g., age), items (e.g., price), and ratings (e.g., rating time). This paper surveys work in the past decade to develop attribute-aware CF systems and finds that they can be classified into four different categories mathematically. We provide readers not only with a high-level mathematical interpretation of the existing work in this area but also with mathematical insight into each category of models. Finally, we provide in-depth experiment results comparing the effectiveness of the major models in each category.https://www.frontiersin.org/article/10.3389/fdata.2019.00049/fullrecommender systemmatrix factorizationcollaborative filteringattributesurvey
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Wen-Hao Chen Chin-Chi Hsu Yi-An Lai Vincent Liu Mi-Yen Yeh Shou-De Lin
spellingShingle	Wen-Hao Chen Chin-Chi Hsu Yi-An Lai Vincent Liu Mi-Yen Yeh Shou-De Lin Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification Frontiers in Big Data recommender system matrix factorization collaborative filtering attribute survey
author_facet	Wen-Hao Chen Chin-Chi Hsu Yi-An Lai Vincent Liu Mi-Yen Yeh Shou-De Lin
author_sort	Wen-Hao Chen
title	Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification
title_short	Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification
title_full	Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification
title_fullStr	Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification
title_full_unstemmed	Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification
title_sort	attribute-aware recommender system based on collaborative filtering: survey and classification
publisher	Frontiers Media S.A.
series	Frontiers in Big Data
issn	2624-909X
publishDate	2020-01-01
description	Attribute-aware CF models aim at rating prediction given not only the historical rating given by users to items but also the information associated with users (e.g., age), items (e.g., price), and ratings (e.g., rating time). This paper surveys work in the past decade to develop attribute-aware CF systems and finds that they can be classified into four different categories mathematically. We provide readers not only with a high-level mathematical interpretation of the existing work in this area but also with mathematical insight into each category of models. Finally, we provide in-depth experiment results comparing the effectiveness of the major models in each category.
topic	recommender system matrix factorization collaborative filtering attribute survey
url	https://www.frontiersin.org/article/10.3389/fdata.2019.00049/full
work_keys_str_mv	AT wenhaochen attributeawarerecommendersystembasedoncollaborativefilteringsurveyandclassification AT chinchihsu attributeawarerecommendersystembasedoncollaborativefilteringsurveyandclassification AT yianlai attributeawarerecommendersystembasedoncollaborativefilteringsurveyandclassification AT vincentliu attributeawarerecommendersystembasedoncollaborativefilteringsurveyandclassification AT miyenyeh attributeawarerecommendersystembasedoncollaborativefilteringsurveyandclassification AT shoudelin attributeawarerecommendersystembasedoncollaborativefilteringsurveyandclassification
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Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

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