  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.
