Exploiting Explicit and Implicit Feedback for Personalized Ranking
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-01-18
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
The problem of the previous researches on personalized ranking is that they focused on either explicit feedback data or implicit feedback data rather than making full use of the information in the dataset.
Until now, nobody has studied personalized ranking algorithm by exploiting both explicit and implicit feedback.
In order to overcome the defects of prior researches, a new personalized ranking algorithm (MERR_SVD++) based on the newest xCLiMF model and SVD++ algorithm was proposed, which exploited both explicit and implicit feedback simultaneously and optimized the well-known evaluation metric Expected Reciprocal Rank (ERR).
Experimental results on practical datasets showed that our proposed algorithm outperformed existing personalized ranking algorithms over different evaluation metrics and that the running time of MERR_SVD++ showed a linear correlation with the number of rating.
Because of its high precision and the good expansibility, MERR_SVD++ is suitable for processing big data and has wide application prospect in the field of internet information recommendation.
American Psychological Association (APA)
Li, Gai& Chen, Qiang. 2016. Exploiting Explicit and Implicit Feedback for Personalized Ranking. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1111884
Modern Language Association (MLA)
Li, Gai& Chen, Qiang. Exploiting Explicit and Implicit Feedback for Personalized Ranking. Mathematical Problems in Engineering No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1111884
American Medical Association (AMA)
Li, Gai& Chen, Qiang. Exploiting Explicit and Implicit Feedback for Personalized Ranking. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1111884
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1111884