A Review on Recommendation Systems for Community Detection in Social Networks and Future Enhancements

Abstract

- A social network is created when individuals communicate with and establish relationships with other people in the community. The amount of interaction between users on the web has increased dramatically with its fast growth. The process of identifying the network’s cohesive groups or clusters is called community detection. Detecting communities in social networks has many practical uses. Recommendation systems improve by grouping similar users through community detection, enabling more personalized content suggestions based on shared interests. In addition to finding individuals with similar interests, communities inside social networks enable us to measure  level of popularity of community. The vast amount of potential data available makes it interesting to dig through social networks for pertinent information. However due to the exponential increase in the number of active members on social networks conventional network analysis methods are becoming ineffective. So, with the aim of identifying merits, demerits and proposing a novel technique for recommendation system to detect communities in social networks, this paper presents a comparative study of recent methods of recommendation systems  and future enhancements and research areas in the field of community detection.