The best preferred product location recommendation according to user context and the preferences

Currently the Smartphones are more popular among the community with the available technologies such as sensor-based interactions and smart apps. The other kinds of trends in such apps lead on context awareness and the personalization for recommending the services for the users based on their context and the preferences. Further, the researches are going on tracking the location of a person and guiding them to the nearby places where the products and the services are available according to their preferences. To accomplish such tasks, tracking and analyzing of the user preferences on different categories of products is required. This paper describes a mobile-based solution; NavToPref where the user preferences and the contextual information are gathered from their mobile phones and recommend and guide them to the nearby locations where the most preferred products are available. Analyzing the metadata of the sites of the frequently and mostly searched products, their top preferred categories of products are identified. This is done by the analysis of the browsing history. Further from their mobile devices, their own contextual information such as whether, location, identified special events from the Google calendar are collected to achieve more personalization on product recommendation. By analyzing the identified preferred products and the user context at the moment, the best preferred product/service locations are notified in the Google map with the shortest path for each product location from the users current location and allows the user to navigate to such locations. If someone is looking for a best promotional deal for shopping, that information is notified along with the recommendation.