Friend Recommendation Mechanism for Social Media Based on Content Matching

This paper introduces a friend recommendation mechanism for social media based on content similarity matching. It can extract information from user's homepage and then analyze the information to generate a group of keywords. Afterwards, it calculates the occurrence frequency of each keyword to form a ranking list. Finally it compares the lists from variety of users and recommends the user with the highest matching result. The paper is organized into five parts: introduction, business discussion, system functional description, methodology and conclusion.