Abstract
Collaborative filtering (CF) is a popular method for the personalized recommendation. Almost all of the existing CF methods rely only on the rating data while ignoring some important implicit information in non-rating properties for users and items, which has a significant impact on the preference. In this study, considering that the average rating of users and items has a certain stability, we firstly propose a personalized fitting pattern to predict missing ratings based on the similarity score set, which combines both the user-based and item-based CF. In order to further reduce the prediction error, we use the non-rating attributes, such as a user’s age, gender and occupation, and an item’s release date and price. Moreover, we present the deviation adjustment method based on the support vector regression. Experimental results on MovieLens dataset show that our proposed algorithms can increase the accuracy of recommendation versus the traditional CF.
| Original language | English |
|---|---|
| Article number | 21 |
| Journal | Human-centric Computing and Information Sciences |
| Volume | 5 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2015 Dec 29 |
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Keywords
- Collaborative filtering
- Deviation adjustment
- Personalized fitting
- Recommendation
ASJC Scopus subject areas
- Computer Science(all)
Cite this
Personalized fitting recommendation based on support vector regression. / Li, Weimin; Li, Xunfeng; Yao, Mengke; Jiang, Jiulei; Jin, Qun.
In: Human-centric Computing and Information Sciences, Vol. 5, No. 1, 21, 29.12.2015.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Personalized fitting recommendation based on support vector regression
AU - Li, Weimin
AU - Li, Xunfeng
AU - Yao, Mengke
AU - Jiang, Jiulei
AU - Jin, Qun
PY - 2015/12/29
Y1 - 2015/12/29
N2 - Collaborative filtering (CF) is a popular method for the personalized recommendation. Almost all of the existing CF methods rely only on the rating data while ignoring some important implicit information in non-rating properties for users and items, which has a significant impact on the preference. In this study, considering that the average rating of users and items has a certain stability, we firstly propose a personalized fitting pattern to predict missing ratings based on the similarity score set, which combines both the user-based and item-based CF. In order to further reduce the prediction error, we use the non-rating attributes, such as a user’s age, gender and occupation, and an item’s release date and price. Moreover, we present the deviation adjustment method based on the support vector regression. Experimental results on MovieLens dataset show that our proposed algorithms can increase the accuracy of recommendation versus the traditional CF.
AB - Collaborative filtering (CF) is a popular method for the personalized recommendation. Almost all of the existing CF methods rely only on the rating data while ignoring some important implicit information in non-rating properties for users and items, which has a significant impact on the preference. In this study, considering that the average rating of users and items has a certain stability, we firstly propose a personalized fitting pattern to predict missing ratings based on the similarity score set, which combines both the user-based and item-based CF. In order to further reduce the prediction error, we use the non-rating attributes, such as a user’s age, gender and occupation, and an item’s release date and price. Moreover, we present the deviation adjustment method based on the support vector regression. Experimental results on MovieLens dataset show that our proposed algorithms can increase the accuracy of recommendation versus the traditional CF.
KW - Collaborative filtering
KW - Deviation adjustment
KW - Personalized fitting
KW - Recommendation
UR - http://www.scopus.com/inward/record.url?scp=84938266338&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84938266338&partnerID=8YFLogxK
U2 - 10.1186/s13673-015-0041-2
DO - 10.1186/s13673-015-0041-2
M3 - Article
AN - SCOPUS:84938266338
VL - 5
JO - Human-centric Computing and Information Sciences
JF - Human-centric Computing and Information Sciences
SN - 2192-1962
IS - 1
M1 - 21
ER -