A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity

The tremendous growth in the amount of attention and users, on social networking sites (SNSs), has led to information overload and that adds to the difficulty of making accurate recommendations of new friends to the users of SNSs. This article incorporates collaborative filtering (CF), the most successful and widely used filtering technique, in social networks to facilitate users in exploring new friends having similar interests while being connected with old ones as well. Here, first we design an implicit rating model, for estimating a user’s affinity toward his friends, which uncover the strength of relationship, utilizing both attribute similarity and user interaction intensity. We then propose a CF-based framework that offers list of friends to the user by leveraging on the preference of like-minded users, with a given small set of people that user has already labeled as friends. Despite the immense success of CF, accuracy and sparsity are still major challenges, especially in social networking domain with a staggering growth having enormous number of users. To address these inherent challenges, first we have explored the idea of adaptive similarity computation between users by employing evolutionary algorithms to learn individual preferences toward particular set of attributes that results in considerable improvement in recommendation accuracy as compared to the situation where all the attributes are given equal importance. Second, we incorporate effective missing data prediction algorithm as a solution to data sparsity thereby further enhancing accuracy. Experimental results are presented to illustrate the effectiveness of the proposed friends recommendation schemes.