An Empirical Study of the Use of Multi-dimensional Contexts for Collaborative-Filtering-Based Service Recommendations in IoT Environments

Engineering the Web in the Big Data Era - Lecture Notes in Computer Science ◽

10.1007/978-3-319-19890-3_65 ◽

2015 ◽

pp. 717-720 ◽

Author(s):

Joo-Sik Son ◽

Han-Gyu Ko ◽

In-Young Ko

Keyword(s):

Empirical Study ◽

Collaborative Filtering

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Metalearning and Recommender Systems: A literature review and empirical study on the algorithm selection problem for Collaborative Filtering

Information Sciences ◽

10.1016/j.ins.2017.09.050 ◽

2018 ◽

Vol 423 ◽

pp. 128-144 ◽

Author(s):

Tiago Cunha ◽

Carlos Soares ◽

André C.P.L.F. de Carvalho

Keyword(s):

Literature Review ◽

Empirical Study ◽

Collaborative Filtering ◽

Recommender Systems ◽

Selection Problem ◽

Algorithm Selection

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Empirical Study of Social Collaborative Filtering Algorithm

Intelligent Information and Database Systems - Lecture Notes in Computer Science ◽

10.1007/978-3-662-49390-8_8 ◽

2016 ◽

pp. 85-95

Author(s):

Firas Ben Kharrat ◽

Aymen Elkhlifi ◽

Rim Faiz

Keyword(s):

Empirical Study ◽

Collaborative Filtering ◽

Filtering Algorithm ◽

Collaborative Filtering Algorithm

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An Empirical Study of Data Smoothing Methods for Memory-Based and Hybrid Collaborative Filtering

Lecture Notes in Computer Science - PRICAI 2006: Trends in Artificial Intelligence ◽

10.1007/978-3-540-36668-3_11 ◽

2006 ◽

pp. 81-90

Author(s):

Dingyi Han ◽

Gui-Rong Xue ◽

Yong Yu

Keyword(s):

Empirical Study ◽

Collaborative Filtering ◽

Data Smoothing ◽

Smoothing Methods ◽

Hybrid Collaborative Filtering

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Impact of clustering on quality of recommendation in cluster-based collaborative filtering: an empirical study

International Journal of Business Intelligence and Data Mining ◽

10.1504/ijbidm.2020.108774 ◽

2020 ◽

Vol 17 (2) ◽

pp. 206

Author(s):

Monika Singh ◽

Monica Mehrotra

Keyword(s):

Empirical Study ◽

Collaborative Filtering

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Empirical Study of Matrix Factorization Methods for Collaborative Filtering

Lecture Notes in Computer Science - Pattern Recognition and Machine Intelligence ◽

10.1007/978-3-642-21786-9_58 ◽

2011 ◽

pp. 358-363

Author(s):

Evgeny Kharitonov

Keyword(s):

Empirical Study ◽

Collaborative Filtering ◽

Matrix Factorization ◽

Factorization Methods

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An Empirical Study on the Effect of Different Similarity Measures on User-Based Collaborative Filtering Algorithms

PRICAI 2008: Trends in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-540-89197-0_108 ◽

2008 ◽

pp. 1065-1070 ◽

Author(s):

Ashish Sureka ◽

Pranav Prabhakar Mirajkar

Keyword(s):

Empirical Study ◽

Collaborative Filtering ◽

Similarity Measures ◽

Filtering Algorithms

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An empirical study on the effect of data sparsity and data overlap on cross domain collaborative filtering performance

Expert Systems with Applications ◽

10.1016/j.eswa.2017.07.041 ◽

2017 ◽

Vol 89 ◽

pp. 254-265 ◽

Author(s):

Hyunwoo Hwangbo ◽

Yangsok Kim

Keyword(s):

Empirical Study ◽

Collaborative Filtering ◽

Data Sparsity ◽

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Impact of Clustering on quality of Recommendation in Cluster based Collaborative Filtering: an Empirical Study

International Journal of Business Intelligence and Data Mining ◽

10.1504/ijbidm.2019.10013538 ◽

2019 ◽

Vol 14 (1) ◽

pp. 1

Author(s):

Monica Mehrotra ◽

MONIKA SINGH

Keyword(s):

Empirical Study ◽

Collaborative Filtering

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An empirical study on user-topic rating based collaborative filtering methods

World Wide Web ◽

10.1007/s11280-016-0412-2 ◽

2016 ◽

Vol 20 (4) ◽

pp. 815-829 ◽

Author(s):

Tieke He ◽

Zhenyu Chen ◽

Jia Liu ◽

Xiaofang Zhou ◽

Xingzhong Du ◽

...

Keyword(s):

Empirical Study ◽

Collaborative Filtering

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An Empirical Study of Data Smoothing Methods for Memory-Based and Hybrid Collaborative Filtering

Lecture Notes in Computer Science - PRICAI 2006: Trends in Artificial Intelligence ◽

10.1007/11801603_11 ◽

2006 ◽

pp. 81-90

Author(s):

Dingyi Han ◽

Gui-Rong Xue ◽

Yong Yu

Keyword(s):

Empirical Study ◽

Collaborative Filtering ◽

Data Smoothing ◽

Smoothing Methods ◽

Hybrid Collaborative Filtering

Download Full-text