Friend recommendation framework for social networking sites using user's online behavior

Hasan, Md. Mehedi; Shaon, Noor Hussain; Marouf, Ahmed Al; Hasan, Md. Kamrul; Mahmud, Hasan; Khan, Md. Mohiuddin

doi:10.1109/iccitechn.2015.7488130

Cited by 13 publications

(5 citation statements)

References 8 publications

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“…The data used is a collection of users and their relationships extracted from Facebook social network. The model achieved an accuracy of 94% [15].…”

Section: Related Workmentioning

confidence: 92%

Dual-Stage Social Friend Recommendation System Based on User Interests

Qader

Abbas

2020

eijs

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The use of online social network (OSN) has become essential to humans' lives whether for entertainment, business or shopping. This increasing use of OSN motivates designing and implementing special systems that use OSN users' data to provide better user experience using machine learning and data mining algorithms and techniques. One system that is used extensively for this purpose is friend recommendation system (FRS) in which it recommends users to other users in professional or entertaining online social networks. For this purpose, this study proposes a novel friend recommendation system, namely Hybrid Friend Recommendation (FR) model. The Hybrid model applies dual-stage methodology on unlabeled data of 1241 users collected from OSN users via our online survey platform featuring user interests and activities based upon which users with similar social behavioral patterns are recommended to each other. The model employs a variety of techniques including user-based collaborative filtering (UBCF) approach and graph-based approach friend-of-friend recommendation (FOF). The model offers unique solutions to common problems of FRS such as data sparsity by using dimensionality technique called non-negative matrix factorization (NMF) to create a dense representation of the collected data and reduce its sparsity as well as providing seamless integration with other FRSs. The evaluation of the hybrid FR model shows positive correlation of Pearson correlation coefficient (PCC) compared to the baseline used.

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“…The data used is a collection of users and their relationships extracted from Facebook social network. The model achieved an accuracy of 94% [15].…”

Section: Related Workmentioning

confidence: 92%

Dual-Stage Social Friend Recommendation System Based on User Interests

Qader

Abbas

2020

eijs

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“…These high scores are due to tag utilization in diverse sharable web-based content such as news, videos, articles, and bookmarks [75]. Researchers may also account for advanced tag features from websites involving the probability matrix trust score, cross-domain tag similarities, and hashtags in blogs based on implicit and explicit information in analyzing the user's specialization/interests [76].…”

Section: Field-normalized Citation Score (Ncsf) Of Smrs Field Categor...mentioning

confidence: 99%

Social Media Recommender Systems (SMRS): A Bibliometric Analysis Study 2000–2021

et al. 2022

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The increasing popularity of social media resources such as blogs, bookmarks, chatrooms, forums and video portals in recent years has attracted diverse users. Following the rise of the Internet, online content has become overloaded, prompting the introduction of recommender systems on social media. As a result, research on the dynamic growth of recommender systems in social media has gained significant traction since the year 2000. Social media recommender systems (SMRS) utilize multiple recommendation fields such as item, user, location, tag, event, tour and game in searching for preferred recommended information. Thus, young research fellows, academic scholars and practitioners must understand the need for SMRS to be complemented with recommendation fields. This requirement underlines the significance of a bibliometric analysis that focuses on social media based on existing publications. Hence, using the Web of Science (WoS) database, this study aimed to gather statistical information on SMRS to help researchers acquire an extensive understanding of such systems. The analysis was conducted by identifying SMRS-related publications and scientometric indicators to assess the growth rate, including the relative growth rate (RGR), doubling time (DT) and the field-normalized citation score (NCSf)-for citation analysis. Overall, this bibliometric study provides relevant measures for comparing and improving the citation rate of publications for new researchers.

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“…In recent years, the impact of social media is significant and computer science as well as psychology researchers are making essential contributions in this area. The use of behavioral features are been integrated in social media such as users behavior based community recommendation [21], strong friends based friend recommendation frameworks [22]. But, none of the work shows the relationships on the perceived stress of the user and his/her personality traits.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning based Approach for Mapping Personality Traits and Perceived Stress Scale of Undergraduate Students

Marouf¹,

Ashrafi²,

Ahmed³

et al. 2019

IJMECS

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This paper focuses on the personality traits of students and stress scale they had to face in undergraduate level. With the advancement of computer science and machine learning based applications, we have tried to inter-correlate the terms. In the area of computational psychology, it is important to understand participants' psychological behavior using personality traits and predict how he/she is going to react on a certain level of the stressed situation. For the experiment, we have collected data of around 150 participants. The personality traits data are collected using the standard survey named The Big Five Personality Test created by IPIP organization and stress scale measurements are collected using scale devised by Sheldon Cohen named as Perceived Stress Scale hosted by Mind garden. The data are taken from Bangladeshi computer science undergraduate students and kept anonymous. In this paper, we have applied nine different machine learning based classification models are built for mapping the traits with stress scales. For performance evaluation, we have utilized precision, recall, f1-score, and accuracy. From the experimental findings, we found that Sequential Minimal Optimization (SMO) and k-NN classifier gives the highest prediction accuracy which is approximately 70%.

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Friend recommendation framework for social networking sites using user's online behavior

Cited by 13 publications

References 8 publications

Dual-Stage Social Friend Recommendation System Based on User Interests

Dual-Stage Social Friend Recommendation System Based on User Interests

Social Media Recommender Systems (SMRS): A Bibliometric Analysis Study 2000–2021

A Machine Learning based Approach for Mapping Personality Traits and Perceived Stress Scale of Undergraduate Students

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