TY - JOUR
T1 - Mining customer product ratings for personalized marketing
AU - CHEUNG, Kwok Wai
AU - Kwok, James T.
AU - Law, Martin H.
AU - Tsui, Kwok Ching
N1 - Funding Information:
This research has been partially supported by the Research Grants Council of the Hong Kong Special Administrative Region under grant HKUST2033/00E and the Hong Kong Baptist University under grant FRG/99-00/II-36P. The EachMovie dataset for this paper was provided by Digital Equipment. The authors would also like to thank Thorsten Joachims for his SVM-Light [25] used in the experiments.
PY - 2003/5
Y1 - 2003/5
N2 - With the increasing popularity of Internet commerce, a wealth of information about the customers can now be readily acquired on-line. An important example is the customers' preference ratings for the various products offered by the company. Successful mining of these ratings can thus allow the company's direct marketing campaigns to provide automatic product recommendations. In general, these recommender systems are based on two complementary techniques. Content-based systems match customer interests with information about the products, while collaborative systems utilize preference ratings from the other customers. In this paper, we address some issues faced by these systems, and study how recent machine learning algorithms, namely the support vector machine and the latent class model, can be used to alleviate these problems.
AB - With the increasing popularity of Internet commerce, a wealth of information about the customers can now be readily acquired on-line. An important example is the customers' preference ratings for the various products offered by the company. Successful mining of these ratings can thus allow the company's direct marketing campaigns to provide automatic product recommendations. In general, these recommender systems are based on two complementary techniques. Content-based systems match customer interests with information about the products, while collaborative systems utilize preference ratings from the other customers. In this paper, we address some issues faced by these systems, and study how recent machine learning algorithms, namely the support vector machine and the latent class model, can be used to alleviate these problems.
KW - Latent class model
KW - Personalized marketing
KW - Recommender systems
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=12244294767&partnerID=8YFLogxK
U2 - 10.1016/S0167-9236(02)00108-2
DO - 10.1016/S0167-9236(02)00108-2
M3 - Article
AN - SCOPUS:12244294767
SN - 0167-9236
VL - 35
SP - 231
EP - 243
JO - Decision Support Systems
JF - Decision Support Systems
IS - 2
ER -