6533b832fe1ef96bd129af0a

RESEARCH PRODUCT

Customer recommendation based on profile matching and customized campaigns in on-line social networks

Simona E. RomboAndrea De SalveMariella BonomoGaspare Ciaccio

subject

Matching (statistics)Word embeddingInformation retrievalSettore INF/01 - InformaticaSocial networkComputer sciencebusiness.industry02 engineering and technologyRecommender systemProfile matchingSocial advertisingRecommendation systemAdvertising campaignSemantic similaritySemantic similarity020204 information systemsSimilarity (psychology)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingbusinessPersonally identifiable information

description

We propose a general framework for the recommendation of possible customers (users) to advertisers (e.g., brands) based on the comparison between On-Line Social Network profiles. In particular, we associate suitable categories and subcategories to both user and brand profiles in the considered On-line Social Network. When categories involve posts and comments, the comparison is based on word embedding, and this allows to take into account the similarity between the topics of particular interest for a brand and the user preferences. Furthermore, user personal information, such as age, job or genre, are used for targeting specific advertising campaigns. Results on real Facebook dataset show that the proposed approach is successful in identifying the most suitable set of users to be used as target for a given advertisement campaign.

yearjournalcountryeditionlanguage
2019-08-27Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
https://doi.org/10.1145/3341161.3345621