Adaptive personalization of social media feed using postcategorization | [International Journal of Communication Networks and Information Security • 2024]

Author(s):

1. N. Gopika Rani: Department of Computer Science and Engineering, PSG College of Technology,,India

2. M. Swetha: Department of Computer Science and Engineering, PSG College of Technology,,India

Abstract:

In today's world, social media constitutes a significant part of everyone's lives. It occupies so much of our time that it even kills our productivity. Social media applications consist of enormous number of posts that can be both informative and entertaining and belong to a wide range of categories. They can be made more user-friendly by the personalization and customization of feeds of users who consume it. Suggestions shown often consist of uninteresting posts, which can make the user experience bad and result in longer scrolling durations. The solution proposed here provides a customized and personalized feed for users based on user interests. Most of the social media applications are proprietary and the algorithms for adaptive personalization are not available publicly. The main objective of this work is to develop a model for deciding how to include or discard a new post from the users' feeds based on user interests collected earlier. The system uses concepts of clustering, informationretrieval and user profiling.

Page(s): 581-586

DOI: DOI not available

Published: Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 4, Year: 2024

Keywords:

personalization , eigenvalues , neuralnetworks , userprofiling , fuzzyclustering

References:

[1] Shawky Doaa .2019 .Towards a Personalized Learning Experience Using Reinforcement Learning”. Studies in Computational Intelligence, (7) : 8.

[2] Peña Paula .2013 .Jorge Vea-Murguía, Carlos González and Sergio Mayo, “Collective Knowledge Ontology User Profiling for Twitter - Automatic User Profiling”. IEEE/WIC/ACM International Conferences on Web Intelligence, : .

[3] Gao Min .2010 .Personalisation in web computing and informatics: Theories, techniques, applications. Information Systems Frontiers, doi:10.1007/s10796-009-9199- 3, : .

[4] Ali Muhammad .2012 .Dynamically Adaptive User Profiling for Personalized Recommendations”. IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, : .

[5] Yi Xun,Fang-Yu Rao,Kwok-Yan Lam .2020 .Privacy-Preserving User Profile Matching in Social Networks”. IEEE Transactions on Knowledge and Data Engineering, 32 : .

[6] . .Ding qi Yang, Bingqing Qu and Philippe Cudré-Mauroux, “Privacy-Preserving Social Media Data Publishing for Personalized Ranking-Based Recommendation”. IEEE Transactions on Knowledge and Data Engineering, 312019 : .

[7] Michael M.,Tadesse M.,Hongfei Lin M. .2018 .Bo Xu and Liang Yang, “Personality Predictions Based on User Behavior on the Facebook Social Media Platform”. IEEE Access, 6 : .

[8] Das Payal,Sajeev, G P .2017 .“Time- Driven Adaptive Web Personalization System for Dynamic Users”. IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), : .

[9] Payal Das,Jisha R. C.,Sajeev G. P. .2017 .Adaptive web personalization system using splay tree”. International Conference on Advances in Computing, Communications and Informatics (ICACCI), : .

[10] N.D. . .Adaptive multimedia content personalization”. IEEE International Symposium on Circuits and Systems, : .

[11] Norouzzadeh Mohammad,Saraee Mohammad .2004 .Web search personalization: A fuzzy adaptive approach”. 2nd IEEE International Conference on Computer Science and Information Technology, : .

[12] .2004 .Virtual product experience: Effects of visual and functional control of products on perceived diagnosticity in electronic shopping”. Journal of Management Information Systems, 11045817(3) : 111-147.

Citations

Downloads

Views