A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and ada
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ORIGINAL ARTICLE
A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity Vinti Agarwal • K. K. Bharadwaj
Received: 31 January 2012 / Revised: 27 July 2012 / Accepted: 11 August 2012 / Published online: 18 September 2012 Springer-Verlag 2012
Abstract 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 likeminded 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 V. Agarwal (&) K. K. Bharadwaj School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India e-mail: [email protected] K. K. Bharadwaj e-mail: [email protected]
are presented to illustrate the effectiveness of the proposed friends recommendation schemes. Keywords Social networking sites Social recommender systems Friend recommender systems Implicit rating model Real-valued genetic algorithm Collaborative filtering Sparsity Missing value prediction
1 Introduction The huge popularity of social networking sites (SNSs) has changed the means of communications as it was not earlier—‘‘by allowing individuals from diverse areas to meet, interact, share interests and more important, establish or maintain connections with others’’. Rather they allow users to express these varied relationships as users are integrated into live social network, through profiles and ties between them. Some examples include Facebook, Google?, Orkut, LinkedIn, Friendster, Multiply and others have drawn hundreds of millions of users, and also the attention of scientists and media.
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