Cosine similarity-based algorithm for social networking recommendation

Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive negative messages as well are circulated between those users. Users form special with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose framework recommending communities based on their preferences; example, community interested certain sports, art, hobbies, diseases, age, case, so on. The is feature extraction algorithm that utilizes user profiling combines cosine similarity measure term frequency recommend communities. Once data received from user, system tracks behavior, relationships identified, then recommends one more preferences. Finally, experimental studies conducted using prototype developed test proposed framework, results show importance of our