Nnon-collaborative interest mining for personal devices

In our daily life we frequently use mobile devices to interact with the people and things on the Internet. However, finding the right things when needed is getting difficult and frustrating. In this paper, we introduce a relatively new problem of non-collaborative personal interest mining using contexts and ratings available for items of interest. We present multi-step algorithms to extract personal situational interests from mobile phone usage logs without depending on other people´s data. The algorithms are based on clustering or a direct analogy from collaborative filtering. We provide extensive experimental results with our accuracy measure for synthetic data sets. The main advantages of our algorithms are: 1) no need for the user to train the phone actively, 2) no need for prior knowledge of the situations contained in a data set, 3) light-weight and running completely on a personal mobile phone and 4) good performance over low data densities. We also present a SmartSearch application. Upon user request, it automatically constructs search queries based on learned user interests and obtains information and advertisements for the user that suit the user´s situation.