Identifying the $k$ Best Targets for an Advertisement Campaign via Online Social Networks

TL;DR

This paper introduces a new method for selecting the top $k$ users for advertising campaigns by combining profile similarity and neighborhood analysis on large social networks, using big data technologies.

Contribution

The paper presents a novel approach that integrates profile matching and neighborhood analysis for targeted advertising, efficiently scalable to large social networks.

Findings

Effective identification of target users using combined profile and neighborhood analysis.

Successful application on real datasets demonstrating improved targeting accuracy.

Scalable implementation leveraging Big Data Technologies.

Abstract

We propose a novel approach for the recommendation of possible customers (users) to advertisers (e.g., brands) based on two main aspects: (i) the comparison between On-line Social Network profiles, and (ii) neighborhood analysis on the On-line Social Network. Profile matching between users and brands is considered based on bag-of-words representation of textual contents coming from the social media, and measures such as the Term Frequency-Inverse Document Frequency are used in order to characterize the importance of words in the comparison. The approach has been implemented relying on Big Data Technologies, allowing this way the efficient analysis of very large Online Social Networks. Results on real datasets show that the combination of profile matching and neighborhood analysis is successful in identifying the most suitable set of users to be used as target for a given advertisement campaign.