6533b833fe1ef96bd129c396

RESEARCH PRODUCT

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

Simona E. RomboMariella BonomoArmando La Placa

subject

Social and Information Networks (cs.SI)FOS: Computer and information sciencesMatching (statistics)Social networkSettore INF/01 - Informaticabusiness.industryComputer scienceBig dataDatabases (cs.DB)AdvertisingComputer Science - Social and Information NetworksOnline Social Networks Social Advertising tf-idf Profile Matching.Term (time)Computer Science - Information RetrievalSet (abstract data type)Computer Science - DatabasesOrder (business)Computer Science - Data Structures and AlgorithmsData Structures and Algorithms (cs.DS)Social mediabusinessRepresentation (mathematics)Information Retrieval (cs.IR)

description

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.

yearjournalcountryeditionlanguage
2020-01-01
10.5220/0010109201930201http://hdl.handle.net/10447/528788