Hybrid recommendations for mobile commerce based on mobile phone features

Chuen He Liou*, Duen-Ren Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish
Pages (from-to)108-123
Number of pages16
JournalExpert Systems
Issue number2
StatePublished - 2012


  • Association rules
  • Collaborative filtering
  • Mobile phone features
  • Mobile web
  • One-to-one marketing
  • Product recommendation


Dive into the research topics of 'Hybrid recommendations for mobile commerce based on mobile phone features'. Together they form a unique fingerprint.

Cite this