At home, on the television, the sheer number of Channels and the vast number of Programs on each Channel has itself made the task of identifying the "appropriate" program to watch difficult for the common user. There is a need of a system to generate suggestions/recommendation to the common user about which Programs to watch and when. In this paper, we propose a method and system which assists the user to choose which Programs on which Channels to watch without any inputs from the Viewer about his "Likes" or "Dislikes". It learns from the Viewer Implicitly over time and learns all the patterns that the Viewer exhibits over the course of Television watching.
In recent years, the amount of online content has grown in enormous proportions. Users try to collect valuable information about contents in order to find their way to relevant web pages. And a lot of research is going on to collect valuable service usage data and process it using different methods to know their behaviors. Many systems and approaches have been proposed in the literature which tries to get information about the user's interests by profiling the user. The objective of the paper is to profile users on their specific devices and the web usage patterns based on the keyboard and mouse usage, time spent on the web. By analyzing the usage patterns of various users, we prove that the patterns exhibited by any one user are different from other users.
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