TY - JOUR
T1 - Hybrid recommendations for mobile commerce based on mobile phone features
AU - Liou, Chuen He
AU - Liu, Duen-Ren
PY - 2012/1/1
Y1 - 2012/1/1
N2 - Mobile data communications have evolved as the number of third generation (3G) subscribers has increased. The evolution has triggered an increase in the use of mobile devices, such as mobile phones, to conduct mobile commerce and mobile shopping on the mobile web. There are fewer products to browse on the mobile web; hence, one-to-one marketing with product recommendations is important. Typical collaborative filtering (CF) recommendation systems make recommendations to potential customers based on the purchase behaviour of customers with similar preferences. However, this method may suffer from the so-called sparsity problem, which means there may not be sufficient similar users because the user-item rating matrix is sparse. In mobile shopping environments, the features of users' mobile phones provide different functionalities for using mobile services; thus, the features may be used to identify users with similar purchase behaviour. In this paper, we propose a mobile phone feature (MPF)-based hybrid method to resolve the sparsity issue of the typical CF method in mobile environments. We use the features of mobile phones to identify users' characteristics and then cluster users into groups with similar interests. The hybrid method combines the MPF-based method and a preference-based method that uses association rule mining to extract recommendation rules from user groups and make recommendations. Our experiment results show that the proposed hybrid method performs better than other recommendation methods.
AB - Mobile data communications have evolved as the number of third generation (3G) subscribers has increased. The evolution has triggered an increase in the use of mobile devices, such as mobile phones, to conduct mobile commerce and mobile shopping on the mobile web. There are fewer products to browse on the mobile web; hence, one-to-one marketing with product recommendations is important. Typical collaborative filtering (CF) recommendation systems make recommendations to potential customers based on the purchase behaviour of customers with similar preferences. However, this method may suffer from the so-called sparsity problem, which means there may not be sufficient similar users because the user-item rating matrix is sparse. In mobile shopping environments, the features of users' mobile phones provide different functionalities for using mobile services; thus, the features may be used to identify users with similar purchase behaviour. In this paper, we propose a mobile phone feature (MPF)-based hybrid method to resolve the sparsity issue of the typical CF method in mobile environments. We use the features of mobile phones to identify users' characteristics and then cluster users into groups with similar interests. The hybrid method combines the MPF-based method and a preference-based method that uses association rule mining to extract recommendation rules from user groups and make recommendations. Our experiment results show that the proposed hybrid method performs better than other recommendation methods.
KW - Association rules
KW - Collaborative filtering
KW - Mobile phone features
KW - Mobile web
KW - One-to-one marketing
KW - Product recommendation
UR - http://www.scopus.com/inward/record.url?scp=84893674425&partnerID=8YFLogxK
U2 - 10.1111/j.1468-0394.2010.00566.x
DO - 10.1111/j.1468-0394.2010.00566.x
M3 - Article
AN - SCOPUS:84893674425
SN - 0266-4720
VL - 29
SP - 108
EP - 123
JO - Expert Systems
JF - Expert Systems
IS - 2
ER -