Hybrid Multiple Channels-Based Recommendations for Mobile Commerce

Mobile data communications have evolved as the number of third generation (3G) subscribers has increased to conduct mobile commerce. Multichannel companies would like to develop mobile commerce but meet difficulties because of lack of knowledge about users' consumption behaviors on the new mobile channel. Typical collaborative filtering (CF) recommendations may suffer from the so-called sparsity problem because few products are browsed on the mobile Web. In this study, we propose a hybrid multiple channels method to resolve the lack of knowledge about users' consumption behaviors on the new channel and the difficulty of finding similar users due to the sparsity problem of the typical CF. Products are recommended to the new mobile channel users based on their browsing behaviors on the new mobile channel as well as consumption behaviors on the existing multiple channels according to different weights. Our experimental results show that the proposed method performs well compared to the other recommendation methods.