A Novel Hybrid Similarity Calculation Model
Joint Authors
Liao, Zhifang
Fan, Xiaoping
Chen, Zhijie
Zhu, Liangkun
Fu, Bencai
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-12-04
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
This paper addresses the problems of similarity calculation in the traditional recommendation algorithms of nearest neighbor collaborative filtering, especially the failure in describing dynamic user preference.
Proceeding from the perspective of solving the problem of user interest drift, a new hybrid similarity calculation model is proposed in this paper.
This model consists of two parts, on the one hand the model uses the function fitting to describe users’ rating behaviors and their rating preferences, and on the other hand it employs the Random Forest algorithm to take user attribute features into account.
Furthermore, the paper combines the two parts to build a new hybrid similarity calculation model for user recommendation.
Experimental results show that, for data sets of different size, the model’s prediction precision is higher than the traditional recommendation algorithms.
American Psychological Association (APA)
Fan, Xiaoping& Chen, Zhijie& Zhu, Liangkun& Liao, Zhifang& Fu, Bencai. 2017. A Novel Hybrid Similarity Calculation Model. Scientific Programming،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1203402
Modern Language Association (MLA)
Fan, Xiaoping…[et al.]. A Novel Hybrid Similarity Calculation Model. Scientific Programming No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1203402
American Medical Association (AMA)
Fan, Xiaoping& Chen, Zhijie& Zhu, Liangkun& Liao, Zhifang& Fu, Bencai. A Novel Hybrid Similarity Calculation Model. Scientific Programming. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1203402
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1203402