Personalized Mobile Searching Approach Based on Combining Content-Based Filtering and Collaborative Filtering

Abstract

Resources in cloud computing platforms such as Amazon, Google AppEngine, and Microsoft Azure are a natural fit to remedy the lack of local resources in mobile devices, which creates a new space of mobile search to improve the availability of cloud resources. In essence, mobile search is a context-aware and personalized activity since mobile devices' inherent movability allows people to retrieve information anytime and anywhere. However, current mobile search products are always far from personalized and are accuracy centered on the convergence of mobile platforms. In this paper, a hybrid filtering mechanism is proposed to eliminate irrelevant or less relevant results for personalized mobile search, which combines content-based filtering and collaborative filtering. The former filters the results according to the mobile user's feature model generated from the user's query history, and the latter filters the results using the user's social network, which is constructed from the user's communication history. Experiments show that the filtering mechanism can significantly improve the user's personalized and precise searching experience on mobile phones.