Mobile commerce product recommendations based on hybrid multiple channels

The number of third generation (3G) subscribers conducting mobile commerce has increased as mobile data communications have evolved. Multi-channel companies that wish to develop mobile commerce face difficulties due to the lack of knowledge about users' consumption behavior on new mobile channels. Typical collaborative filtering (CF) recommendations may be affected by the so-called sparsity problem because relatively few products are browsed or purchased on the mobile Web. In this study, we propose a hybrid multiple channel method to address the lack of knowledge about users' consumption behavior on a new channel and the difficulty of finding similar users due to the sparsity problem of typical CF recommender systems. Products are recommended to users based on their browsing behavior on the new mobile channel as well as the consumption behavior of heavy users of existing channels, such as television, catalogs, and the Web. Our experiment results show that the proposed method performs well compared to the other recommendation methods.