Intelligent cluster connectionist recommender system using implicit graph friendship algorithm for social networks

Abstract

Implicit clusters are formed as a result of the many interactions between users and their contacts. Online social platforms today provide special link-types that allows effective communication. Thus, many users can hardly categorize their contacts into groups such as “family”, “friends” etc. However, such contact clusters are easily represented via implicit graphs. This has arisen the need to analyze users’ implicit social graph and enable the automatic add/delete of contacts from/to a group via a suggestion algorithm – making the group creation process dynamic (instead of static, where users are manually added or removed). The study implements the friend suggest algorithm, which analyzes a user’s implicit social graph to create custom contact group using an interaction-based metric to estimate a user’s affinity to his contacts and groups. The algorithm starts with a small seed set of contacts – already categorized by a user as friends/groups; And, then suggest other contacts to be added to a group. The result inherent demonstrates the importance of both the implicit group relationships and the interaction-based affinity in suggesting friends.