Constructing mobile-oriented catalog in m-commerce using LDA-based self-adaptive genetic algorithm

The purpose of this paper is to develop a method to recommend products to customer via mobile devices. Collaborative recommendation is known as an effective way to recommend products. In this paper, we use the concept of collaborative recommendation to develop Mobile-Oriented Catalog (MOC). The proposed method is made from aggregating similar purchasing records to optimize combination of goods on mobile devices. This paper illustrates how to design attractive and collaborative catalog to recommend items by using Latent Dirichlet Allocation (LDA) based self-adaptive genetic algorithm (LDA-SAGA). LDA-SAGA is consisted of topic modeling concept and self-adaptive genetic algorithm. We use LDA as our topic modeling algorithm to construct MOC as a result that it is the simplest topic model. Our experimental evaluation on synthetic and real data shows that using preference as topic concept is effective. LDA-SAGA is especially outstanding with large number of customers and products. Finally, we compare the MOC which is used on mobile application (APP) of Amazon with the one used on Taobao and discuss the characteristics of their design. Different design of user interface on APP can lead to different scope of fitness value which is capable of explaining different market strategies of Taobao and Amazon.