Dual-Stage Social Friend Recommendation System Based on User Interests


The use of online social network (OSN) has become essential to humans' lives whether for entertainment, business or shopping. One system that is used extensively for this purpose is friend recommendation system (FRS) which recommends users to other users in professional or entertaining online social networks. In this paper, a Dual-Stage Friend Recommendation (FR) model is proposed. The model applies dual-stage methodology on unlabeled data of 1241 users collected from OSN users via 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 techniques including user-based collaborative filtering (UBCF) approach in stage one and graph-based approach friend-of-friend recommendation (FOF) in stage two. The model offers a solution to common problems of FRS, such as data sparsity, using a dimensionality technique called non-negative matrix factorization (NMF) to create a dense representation of the collected data and reduce its sparsity, in addition to providing seamless integration with other FRSs. The evaluation of the FR model shows positive correlation of Pearson correlation coefficient (PCC) as compared the outcomes of using Cosine similarity and Euclidean distance as a baseline.