Similarity recognition using context-based pattern for cyber-society

Abstract

To measure the similarity of contexts in smart devices, comparison is made of the user-defined contexts and another context which is defined in a server or in a network device that a user has (Segev and Toch IEEE Trans Serv Comput 2(3):210-222, 2009). While it processes to compare, if they find some users who have similarities with them, they surely may be interested in the users, because they know that they can share their information without the time wasting to search, finally getting what they want. However, according to the characteristics of the registered contexts, they are classified into two types, a rank-definitional context and a rank-undefined context. Also, the users usually want to use the two types to get quickly what they want at the same time. It means that another algorithm may be needed to get the similarity depending on the contexts, because the existing similarity search algorithms usually perform the similarity process without the contexts' characteristics analysis. They assume that all contexts have the same features when they process. Now, the existing methods that find the similarity usually have an accuracy problem. Low accuracy gives invisible services to users. This paper has suggested named context-based pattern measurement method including weight defines for higher accuracy. As a result, it would be able to get accuracy similarity by applying to the proposed algorithms about 69.072 % without weight and also 95.322 % accuracy in case it has a specific weight.