A Semantic Approach for User-Brand Targeting in On-Line Social Networks
This work addresses targeted advertising in social networks, presenting an incremental improvement through semantic profile comparison.
The authors tackled the problem of recommending potential customers to advertisers by comparing user and brand profiles represented as category trees in online social networks, achieving successful identification of suitable target users for advertisement campaigns.
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 represent both user and brand profiles as trees where nodes correspond to categories and sub-categories in the associated 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 topics popular in the brand profile and user preferences. Results on real datasets show that our approach is successfull in identifying the most suitable set of users to be used as target for a given advertisement campaign.