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Predicting Audience Location on the Basis of the k-Nearest Neighbor Multilabel Classification
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-12-23
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Understanding audience location information in online social networks is important in designing recommendation systems, improving information dissemination, and so on.
In this paper, we focus on predicting the location distribution of audiences on YouTube.
And we transform this problem to a multilabel classification problem, while we find there exist three problems when the classical k-nearest neighbor based algorithm for multilabel classification (ML-kNN) is used to predict location distribution.
Firstly, the feature weights are not considered in measuring the similarity degree.
Secondly, it consumes considerable computing time in finding similar items by traversing all the training set.
Thirdly, the goal of ML-kNN is to find relevant labels for every sample which is different from audience location prediction.
To solve these problems, we propose the methods of measuring similarity based on weight, quickly finding similar items, and ranking a specific number of labels.
On the basis of these methods and the ML-kNN, the k-nearest neighbor based model for audience location prediction (AL-kNN) is proposed for predicting audience location.
The experiments based on massive YouTube data show that the proposed model can more accurately predict the location of YouTube video audience than the ML-kNN, MLNB, and Rank-SVM methods.
American Psychological Association (APA)
Wu, Haitao& Ying, Shi. 2014. Predicting Audience Location on the Basis of the k-Nearest Neighbor Multilabel Classification. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1046494
Modern Language Association (MLA)
Wu, Haitao& Ying, Shi. Predicting Audience Location on the Basis of the k-Nearest Neighbor Multilabel Classification. Mathematical Problems in Engineering No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1046494
American Medical Association (AMA)
Wu, Haitao& Ying, Shi. Predicting Audience Location on the Basis of the k-Nearest Neighbor Multilabel Classification. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1046494
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1046494