Deep Contextual Learning for Event-Based Potential User Recommendation in Online Social Networks

Abstract

Event recommendation allows people to identify various recent upcom-ing social events. Based on the Profile or User recommendation people will identify the group of users to subscribe the event and to participate, despite it faces cold-start issues intrinsically. The existing models exploit multiple contextual factors to mitigate the cold-start issues in essential applications on profile recommendations to the event. However, those existing solution does not incorporate the correlation and covariance measures among various contextual factors. Moreover, recommending similar profiles to various groups of the events also has not been well analyzed in the existing literature. The proposed prototype model Correlation Aware Deep Contextual Learning (CADCL) solves the mentioned issues. CADCL explores correlation on the different perspectives of user and event features to alleviate the sparsity problem. Latent Dirichlet Allocation (LDA) has been employed to extract the latent contextual information to increase the high rele-vancy rate in a hidden layer of the deep learning architecture. Finally, decision of the profile recommendation to the events is integrated on basis of influence weight to the correlation. Experimental analysis of the proposed architecture on Meetup dataset is cross-validated and performance metrics such as Precision 0.99%, Recall 0.88% and F Measure 0.93% are proved to be better on comparing with current state of art approaches.