Geetharani, S and Vadivambigai, S (2022) Recommending Profiles To Social Event Participation In Online Social Network Service Applications Using Deep Learning Techniques. Journal of Pharmaceutical Negative Results, 13 (9). pp. 2032-2043.

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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 interestbased 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.

Item Type: Article
Uncontrolled Keywords: Event Driven Social Networking, Prediction, Deep Learning, Recurrent Neural Network
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 15 Mar 2023 09:26
Last Modified: 15 Mar 2023 09:26
URI: http://ir.psgcas.ac.in/id/eprint/1780

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