With the advent of smartphones, mobile phones have evolved from a simple communication tool to a multipurpose device that affects every aspect of our daily life. The expansion of the mobile application market has made it difficult for smartphone users to find applications that fit their needs. Most prior research on application recommendation provides a limited solution to the problem of application overload. These recommendation techniques, developed outside of the mobile environment, have a number of limitations such as cold start problem and domain disparity. In this paper, we propose AppTrends, which incorporates a graph-based technique for application recommendation in the Android OS environment. Our experiment results obtained from the field usage record of over 4 million applications clearly show that the proposed graphbased recommendation model is more accurate than the Slope One Model.
The data derived from the social tagging system, known as folksonomy, is a potentially useful source for understanding users' intentions. This study seeks to uncover some of the unexplored areas of folksonomy and examine the plausibility of new ideas for the improvement of personalized search. In particular, we challenge several state-of-the-art algorithms by exploiting folksonomy network structures used in creating user profiles that are adaptive and aware of multiple interests of a user, for the personalization of search results. The results obtained from the proposed approach shows a unanimous increase in the performance of personalization when compared to other state-of-the-art algorithms.
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