A Product Recommendation System Based On Mobile Context

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ABSTRACT In this paper, we develop a Product recommendation system based on mobile context aware services to provide customized information for users. We analyze the service satisfaction ratings of the users to recommend favored products for them. With mobile context awareness, the proposed framework can enhance the capacity to satisfy the user demands for product recommendations.

Mobile context aware services

1. INTRODUCTION It is a fact that most of these Apps only use location Based Services (LBS) to help users find the interested location, but yield a lot of irrelevant data. There are two types of recommendations on the Internet, content-based filtering and collaborative Filtering. The former produces results based on the
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it is not convincing enough to employ the (a publicly available film rating data set) for conducting experiments ,since film ratings are quite different from web service QOS values. Shao et al. proposed a user-based PCC method for the web service QOS value prediction and conduct experiments on 20 web services. et al. combined the user-based and item-based approaches to achieve better prediction accuracy and used PCC for the similarity measure. However, as shown in our experiments, the performance of the PCC for the similarity measure is not good enough. Moreover, the experiments only include 150 users and 100 services, which is too small compared with real-world situations.

We introduce the McDonald recommendation system in the following three steps. The first is to collect the information of the user’s information and preferences. The second step is a recommendation based on the use of information or preferences. The third step is to refine the filtering data in terms of the recommended results. Fig. 2 shows the system flow of the proposed method.


Figs. 1 and 2 show a recommendation system built on mobile devices. The development of the system can be divided into two major parts, namely the mobile device (Client-side) and the server (Server end). The client side is implemented on an Android. The server-side uses an Glassfish server and a MySQL
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