An Integrated Framework for Friend Recommender System based on Graph Theoretic Approach

Abstract

Study of relationships established in social media is an emerging area of research. Online Social Network (OSN) is a collection of social entities carrying a lot of information that enriches the network. A structured modelling of the OSN dataset is required for informative knowledge mining and efficient Social Network Analysis (SNA). Graphical representation of data helps in analysing the structural properties, study of dense substructure, cluster formation and identifying the numerous types of entities exhibiting associations based on different activity fields. This paper discusses about various ways of graph theoretic representations of OSN including structure-based and content or interaction-based approaches. An integrated framework is proposed in this paper that learns from various user attributes and its associated interactions, network structure, timeline history, etc from a polarized OSN Graph for generating an efficient Friend Suggestion Recommender System.

Recommendation of Influenced Products Using Association Rule Mining: Neo4j as a Case Study

“…This method genuinely improves the performance of the model. Recommendation systems are also used in different business domains such as E-learning platform [9,10], friend recommendation [11], transportation system [12], energy management [13] etc.…”