User identification based on multiple attribute decision making in social networks

Social networks are becoming increasingly popular and influential, and users are frequently registered on multiple networks simultaneously, in many cases leaving large quantities of personal information on each network. There is also a trend towards the personalization of web applications; to do this, the applications need to acquire information about the particular user. To maximise the use of the various sets of user information distributed on the web, this paper proposes a method to support the reuse and sharing of user profiles by different applications, and is based on user profile integration. To realize this goal, the initial task is user identification, and this forms the focus of the current paper. A new user identification method based on Multiple Attribute Decision Making (MADM) is described in which a subjective weight-directed objective weighting, which is obtained from the Similarity Weight method, is proposed to determine the relative weights of the common properties. Attribute Synthetic Evaluation is used to determine the equivalence of users. Experimental results show that the method is both feasible and effective despite the incompleteness of the candidate user dataset.