Do You Like What I Like? Similarity Estimation in Proximity-based Mobile Social Networks

TL;DR

This paper presents a space-efficient, privacy-preserving method for estimating similarity between users in proximity-based mobile social networks using probabilistic data structures, demonstrated with musical taste data.

Contribution

It introduces a novel approach using Counting Bloom Filters and Count-Min Sketches for fast, accurate similarity estimation without third-party servers.

Findings

Single exchange of probabilistic data structures estimates similarity accurately.

A Counting Bloom Filter with 128 length and one hash function suffices.

Method is generalizable to other multiset similarity estimations.

Abstract

While existing social networking services tend to connect people who know each other, people show a desire to also connect to yet unknown people in physical proximity. Existing research shows that people tend to connect to similar people. Utilizing technology in order to stimulate human interaction between strangers, we consider the scenario of two strangers meeting. On the example of similarity in musical taste, we develop a solution for the problem of similarity estimation in proximity-based mobile social networks. We show that a single exchange of a probabilistic data structure between two devices can closely estimate the similarity of two users - without the need to contact a third-party server.We introduce metrics for fast and space-efficient approximation of the Dice coefficient of two multisets - based on the comparison of two Counting Bloom Filters or two Count-Min Sketches. Our analysis shows that utilizing a single hash function minimizes the error when comparing these probabilistic data structures. The size that should be chosen for the data structure depends on the expected average number of unique input elements. Using real user data, we show that a Counting Bloom Filter with a single hash function and a length of 128 is sufficient to accurately estimate the similarity between two multisets representing the musical tastes of two users. Our approach is generalizable for any other similarity estimation of frequencies represented as multisets.