Open Access

ARTICLE

Personalized Information Retrieval from Friendship Strength of Social Media Comments

Fiaz Majeed¹, Noman Yousaf², Muhammad Shafiq^3,*, Mohammed Ahmed Basheikh⁴, Wazir Zada Khan⁵, Akber Abid Gardezi⁶, Waqar Aslam⁷, Jin-Ghoo Choi³

1 Department of Software Engineering, University of Gujrat, Gujrat, 50700, Pakistan
2 Department of Computer Science, University of Gujrat, Gujrat, 50700, Pakistan
3 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea
4 Computer Science and Artificial Intelligence Department, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
5 Faculty of CS & IT, Jazan University, Jazan, 45142, Saudi Arabia
6 Department of Computer Science, COMSATS University Islamabad, Pakistan
7 Department of Computer Science & IT, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

* Corresponding Author: Muhammad Shafiq. Email:

(This article belongs to the Special Issue: Soft Computing Methods for Innovative Software Practices)

Intelligent Automation & Soft Computing 2022, 32(1), 15-30. https://doi.org/10.32604/iasc.2022.015685

Received 02 December 2020; Accepted 26 April 2021; Issue published 26 October 2021

Abstract

Social networks have become an important venue to express the feelings of their users on a large scale. People are intuitive to use social networks to express their feelings, discuss ideas, and invite folks to take suggestions. Every social media user has a circle of friends. The suggestions of these friends are considered important contributions. Users pay more attention to suggestions provided by their friends or close friends. However, as the content on the Internet increases day by day, user satisfaction decreases at the same rate due to unsatisfactory search results. In this regard, different recommender systems have been developed that recommend friends to add topics and many other things according to the seeker’s interests. The existing system provides a solution for personalized retrieval, but its accuracy is still a problem. In this work, we have proposed a personalized query recommendation system that utilizes Friendship Strength (FS) to recommend queries. For FS calculation, we have used the Facebook dataset comprising of more than 22k records taken from four different accounts. We have developed a ranking algorithm that provides ranking based on FS. Compared with existing systems, the proposed system can provide encouraging results. Key research groups and organizations can use this system for personalized information retrieval.

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Keywords

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