DocumentCode
1937923
Title
Notice of Retraction
Collaborative filtering based on time division
Author
Yan Yang ; Long Yun
Author_Institution
Dept. of Comput. Sci. & Technol., Heilongjiang Univ., Harbin, China
Volume
9
fYear
2010
fDate
9-11 July 2010
Firstpage
312
Lastpage
316
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Collaborative filtering techniques are widely used in e-commerce systems. However, the rating data are very sparse, which affects prediction accuracy greatly. A time division based collaborative filtering algorithm is proposed in this paper. According to the current time and the released time of items, items are divided into three types, named new item, normal item and old item respectively. Accordingly, users´ interests are calculated in the three types respectively and user-item matrix is expanded. When predicting the rate of an item for a user, different strategies are used with different types of items. Data sparsity and new item problems are solved effectively. Empirical studies on MovieLens have shown that our newly proposed method improve recommendation quality obviously.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Collaborative filtering techniques are widely used in e-commerce systems. However, the rating data are very sparse, which affects prediction accuracy greatly. A time division based collaborative filtering algorithm is proposed in this paper. According to the current time and the released time of items, items are divided into three types, named new item, normal item and old item respectively. Accordingly, users´ interests are calculated in the three types respectively and user-item matrix is expanded. When predicting the rate of an item for a user, different strategies are used with different types of items. Data sparsity and new item problems are solved effectively. Empirical studies on MovieLens have shown that our newly proposed method improve recommendation quality obviously.
Keywords
filtering theory; groupware; prediction theory; recommender systems; time series; MovieLens; collaborative filtering algorithm; data rating; data sparsity; e-commerce systems; prediction accuracy; recommendation quality; time division; user-item matrix; Cities and towns; Collaborative Filtering; Data Dparsity; Recommendation; Time;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5563977
Filename
5563977
Link To Document