Recommending Profiles To Social Event Participation In Online Social Network Service Applications Using Deep Learning Techniques

Abstract

Nowadays, organizing online Social events is growing especially in current pandemic situation due to corona. Thus social network services helps to organize the event oriented online social gathering. The events discussed on social networks can be associated with topics, locations, and time periods. Various literatures have employed clustering mechanism to group the profiles of the user of social networking applications. More research has been carried out in event based online network services as existing models leads to scalability and sparsity problems. Event-based online social networks are used to maintain interest-based groups with high relevancy rate, recommendation quality and predictive accuracy. In order to achieve the above goal, in this paper, we propose a novel framework named as Deep Influence Predict (DIP) which explores the features of Recurrent Neural Network in order identify the target or potential users through different patterns and behaviours of the profile on the social networking service applications. It learns multiple levels of representations and abstractions of the latent data through individual participation record. Further, it extracts the extrinsic and intrinsic properties of the profiles on their social connections and social effects. Specifically, it identifies the distinguishing social groups with different topics and categories as multifaceted interest in iterative process. Finally decision of recommendation for the event is integrated on outcomes of user behaviour model through personnel impact, social relation and equilibrium. Evaluation of the proposed model through various case studies has been implemented using hadoop architecture and validated across various measures such as accuracy on precision, Recall and f measure along scalability and Execution time.