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.
Highlights
The widespread use of social media applications is becoming increasingly important as more users enjoy sharing their experiences and daily activities via rating and reviewing products, posting opinions, expressing mood, and even making new friendships
Most of the friend suggestion systems used in social media services such as MySpace, Facebook, LinkedIn, and Twitter, recommend friends based on network graphs using PYMK features (People You May Know)
It combines the advantages of user-based collaborative filtering (UBCF) approach and graph-based (FoF) approach to get more accurate friend recommendation
Summary
The widespread use of social media applications is becoming increasingly important as more users enjoy sharing their experiences and daily activities via rating and reviewing products, posting opinions, expressing mood, and even making new friendships. There are several types of RS; one type is an item recommendation system that suggests products (e.g., movies, books, music, and so on) to customers by taking others users rating products in the past and features of customers interests to create a new recommendation list, such as that applied in Amazon Another common recommendation system is the Friends recommendation system (FRS). One can define the user’s interests as the activities that users enjoy doing and the topics that they like to spend extra time learning about [10], such as specific topics (movies, books, sports, video games, and music), or even joining social groups All these factors can be collected to make friends recommendation decision among social users. Friend-of-friend algorithm is applied to the initial graph to enhance the overall recommendation process (stage two) by assuming that, for instance, U1and U2 are friends with similar interest, and U2 and U3 are friends with similar interest too, U1 and U3 are possibly friends as a result
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