An Anti_Turing Test: Reduced Variables for Social Network Friends' Recommendations

TL;DR

This paper investigates the variables influencing friend recommendations in social networks, proposing algorithms that incorporate randomization and interestingness criteria, and presents a software tool for simulation based on real data.

Contribution

It introduces novel algorithms for friend recommendation that include randomization and interestingness, along with a software tool for simulation and analysis.

Findings

Algorithms effectively simulate recommendation lists

Randomization improves recommendation diversity

The tool models time-dependent recommendation characteristics

Abstract

A routine activity of social networks servers is to recommend candidate friends that one may know and stimulate addition of these people to one's contacts. An intriguing issue is how these recommendation lists are composed. This work investigates the main variables involved in the recommendation activity, in order to reproduce these lists including its time dependent characteristics. We propose relevant algorithms. Besides conventional approaches, such as friend_of_a_friend, two techniques of importance have not been emphasized in previous works: randomization and direct use of interestingness criteria. An automatic software tool to implement these techniques is proposed. Its architecture and implementation are discussed. After a preliminary analysis of actual data collected from social networks, the tool is used to simulate social network friends' recommendations.