Department of Computer Science and Information Systems

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Author: search.filters.author.Agarwal, VintiSubject: search.filters.subject.Friend recommender systems

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    Friends Recommendations in Dynamic Social Networks
    (Springer, 2014-01) Agarwal, Vinti
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    Trust-Enhanced Recommendation of Friends in Web Based Social Networks Using Genetic Algorithms to Learn User Preferences
    (Springer, 2011) Agarwal, Vinti
    Web-based social networks (WBSNs) are a promising new paradigm for large scale distributed data management and collective intelligences. But the exponential growth of social networks poses a new challenge and presents opportunities for recommender systems, such as complicated nature of human to human interaction which comes into play while recommending people. Web based recommender systems (RSs) are the most notable application of the web personalization to deal with problem of information overload. In this paper, we present a Friend RS for WBSNs. Our contribution is three fold. First, we have identified appropriate attributes in a user profile and suggest suitable similarity computation formulae. Second, a real-valued Genetic algorithm is used to learn user preferences based on comparison of individual features to increase recommendation effectiveness. Finally, inorder to alleviate the sparsity problem of collaborative filtering, we have employed trust propagation techniques. Experimental results clearly demonstrate the effectiveness of our proposed schemes.
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    A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity
    (Springer, 2012-09) Agarwal, Vinti
    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.