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Improving Top-N Recommendation Performance Using Missing Data
Joint Authors
Zhao, Xiangyu
Niu, Zhendong
Wang, Kaiyi
Niu, Ke
Liu, Zhongqiang
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-09-07
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Recommender systems become increasingly significant in solving the information explosion problem.
Data sparse is a main challenge in this area.
Massive unrated items constitute missing data with only a few observed ratings.
Most studies consider missing data as unknown information and only use observed data to learn models and generate recommendations.
However, data are missing not at random.
Part of missing data is due to the fact that users choose not to rate them.
This part of missing data is negative examples of user preferences.
Utilizing this information is expected to leverage the performance of recommendation algorithms.
Unfortunately, negative examples are mixed with unlabeled positive examples in missing data, and they are hard to be distinguished.
In this paper, we propose three schemes to utilize the negative examples in missing data.
The schemes are then adapted with SVD++, which is a state-of-the-art matrix factorization recommendation approach, to generate recommendations.
Experimental results on two real datasets show that our proposed approaches gain better top-N performance than the baseline ones on both accuracy and diversity.
American Psychological Association (APA)
Zhao, Xiangyu& Niu, Zhendong& Wang, Kaiyi& Niu, Ke& Liu, Zhongqiang. 2015. Improving Top-N Recommendation Performance Using Missing Data. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1073664
Modern Language Association (MLA)
Zhao, Xiangyu…[et al.]. Improving Top-N Recommendation Performance Using Missing Data. Mathematical Problems in Engineering No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1073664
American Medical Association (AMA)
Zhao, Xiangyu& Niu, Zhendong& Wang, Kaiyi& Niu, Ke& Liu, Zhongqiang. Improving Top-N Recommendation Performance Using Missing Data. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1073664
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1073664