<![CDATA[Social media sites (SMSs) become essential platforms used by people, customers, and companies for communication and marketing. Social media networks (SMNs) allow access to individuals who share their information and this, in turn, can lead to build users' profiles. Profiles can consist of a basic description of users' characteristics such as name, age, gender, education, marital status, email, phone number, and location. Preferences, on the other side, describe users' behavior on SMNs. Earlier literature defined a user's identity in different networks based on matching his/her name only. This research, however, proposes an integrated approach for discovering the nearest users by considering profiles and preferences. The proposed approach includes three key steps. First, users are grouped based on their preferences such as personal interests. Second, properties’ values of a user profile and preferences are integrated to identify the nearest users. Finally, the nearest users to a certain user are identified by measuring the boundary distance. The findings show that the proposed approach can effectively identify the nearest users. Comparing the performance of the proposed approach with the two highly adopted approaches that rely on either users' profiles or preferences suggests that the proposed approach in this research is less prone to error.]]>
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