Abstract
This paper proposes some basic methods of content searching with gaining high user utility or user's satisfaction from a lot of information sources scattered in large scale networks. In these methods, searching policy is decided by the estimated user utility gain and/or current user utility for each searching action, and the most favorable combination of an information source and searching content item is selected so as to enlarge the total amount of user utility of whole searching activity. In addition, we evaluate the efficiency of the methods by computer simulations; in which, we give network topology, user utility functions, existing probabilities of searching content items in each information source etc., apply proposed methods and other methods for comparison, and compare summation of their utility gains as an estimative index. As a result, the proposed methods achieve good performance in general and about 2.45 times larger index value than an ordinary searching is observed in an extreme simulation scenario. Furthermore, we discuss pros and cons of the proposed methods in all the simulation scenarios.
