Maximizing Acceptance Probability for Active Friending in On-Line Social Networks

TL;DR

This paper introduces a novel active friending recommendation method that guides users to maximize the acceptance probability of friend invitations, validated through implementation and user studies on Facebook.

Contribution

It formulates the Acceptance Probability Maximization problem and proposes an efficient polynomial-time algorithm, SITINA, for optimal active friending guidance.

Findings

SITINA outperforms manual selection in solution quality.

The approach effectively increases acceptance probability in active friending.

Implementation on Facebook demonstrates practical applicability.

Abstract

Friending recommendation has successfully contributed to the explosive growth of on-line 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 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 in existing on-line 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, \emph{Acceptance Probability Maximization (APM)}, and develop a polynomial time algorithm, called \emph{Selective Invitation with Tree and In-Node Aggregation (SITINA)}, to find the optimal solution. We implement an active friending service with SITINA in Facebook to validate our idea. Our user study and experimental results manifest that SITINA outperforms manual selection and the baseline approach in solution quality efficiently.