Dual-Stage Social Friend Recommendation System Based on User Interests

Abstract

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.

Future Friend Recommendation System based on User Similarities in Large-Scale on Social Network

“…In 2020, Qader et al [11] suggested a Dual-Stage FR model to recommends users to other users based on user interests. The model applies the double stage technique on unlabeled information of 1241 users collected from OSN users via the online survey.…”

“…That indicates 97% users on our dataset are recommended accurately by our proposed system. Moreover, to compare the proposed model with other existing methods, we have re-implemented the models used in [8,10,11,12,13,14,16] as accordance with the description in the paper to make a fair comparison. All the models are evaluated on the same data set to ensure the fairness of the comparison.…”

“…Graphical comparisons of proposed model with [8], [10], [11], [12], [13], [14], and [16] has shown in Fig. 6.…”

“…In the future, we will spread this work by combining attributes, for example, shares, comments, likes, pictures, status, [11] 2020 90% Soni et al [12] 2020 91.5% Kumar et al [13] 2018 93% Al-Sabaawi et al [14] 2018 94% Berkani et al [16] 2021 93.5% Proposed Method -97% Fig. 7.…”

Enhancement of Recommendation Engine Technique for Bug System Fixes

“…The study utilizes datasets sourced from the same opensource project as Eclipse. A recommendation engine [5][6][7] is employed, incorporating optimization techniques through utilizing various machine learning algorithms, including C4.5, neural networks, deep learning architectures, and optimization techniques using the most recent evolutionary algorithm. Several researchers utilized support vector machines and explored the application of unsupervised learning techniques.…”