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


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.

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR)
Mariella Bonomo
PhD student

Phd Student

Armando La Placa
PhD student

PhD Student

Simona E. Rombo
Associate professor

Researcher on Bioinformatics, Network Analysis, Big Data