Maximizing Acceptance Probability for Active Friending in Online Social Networks - Yang, De-Nian; Hung, Hui-Ju; Lee, Wang-Chien; Chen, Wei (In Proceedings of 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'2013), Chicago, Illinois, U.S.A., August, 2013., 2013)
Friending recommendation has successfully contributed to the explosive growth of online social networks. Most friending recommendation services today aim to support passive friending, where a user passively selects friending targets from the recommended candidates. In this paper, we advocate a recommendation support for active friending, where a user actively speciﬁes a friending target. To the best of our knowledge, a recommendation designed to provide guidance for a user to systematically approach his friending target has not been explored for existing online social networking services. To maximize the probability that the friending target would accept an invitation from the user, we formulate a new optimization problem, namely, Acceptance Probability Maximization (APM), and develop a polynomial time algorithm, called Selective Invitation with Tree and In-Node Aggregation (SITINA), to ﬁnd the optimal solution. We implement an active friending service with SITINA on Facebook to validate our idea. Our user study and experimental results reveal that SITINA outperforms manual selection and the baseline approach in solution quality eﬃciently.