TY - GEN
T1 - Personalized fitting with deviation adjustment based on support vector regression for recommendation
AU - Li, Weimin
AU - Yao, Mengke
AU - Jin, Qun
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2014.
PY - 2014
Y1 - 2014
N2 - Almost all of the existing Collaborative Filtering (CF) methods rely only on the rating data while ignoring some important implicit information in non-rating properties for users and items, which have a significant impact on the preference. In this study, considering that the average rating of users and items have a certain stability, we firstly propose a personalized fitting pattern to predict missing ratings based on the trusty score set, which combines both the user-based CF 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 (SVR). Experiment results show that our proposed algorithms can increase the accuracy of recommendation versus the traditional CF.
AB - Almost all of the existing Collaborative Filtering (CF) methods rely only on the rating data while ignoring some important implicit information in non-rating properties for users and items, which have a significant impact on the preference. In this study, considering that the average rating of users and items have a certain stability, we firstly propose a personalized fitting pattern to predict missing ratings based on the trusty score set, which combines both the user-based CF 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 (SVR). Experiment results 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=84924345950&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84924345950&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-54900-7_25
DO - 10.1007/978-3-642-54900-7_25
M3 - Conference contribution
AN - SCOPUS:84924345950
T3 - Lecture Notes in Electrical Engineering
SP - 173
EP - 178
BT - Multimedia and Ubiquitous Engineering
A2 - Chen, Shu-Ching
A2 - Park, James J.
A2 - Yen, Neil Y.
A2 - Gil, Joon-Min
PB - Springer Verlag
T2 - FTRA 8th International Conference on Multimedia and Ubiquitous Engineering, MUE 2014
Y2 - 28 May 2014 through 31 May 2014
ER -