Users engaging in online social networks provide sparse data about themselves, e.g. by participating in groups to discuss some topics, linking to each other, etc. Such sparse data can be carefully used to build both user and group profiles, automatically. We put forward a multi-agent system that collects and analyses data scattered on an online social network. The analysis aims at characterising both users, by inserting them into categories, and groups, with a set of key words. The user classification technology is an especially devised neural network that extracts relevant characteristics from raw data characterising user behaviour, and then provides for unknown users the most likely category. Thanks to the said classification tool, some online activities performed by a given user that are unusual for such a user are automatically detected. Moreover, according to the user interests, contents inserted on public pages, which the user is unaware of, can be automatically found and suggested.
|Titolo:||An AOP-RBPNN approach to infer user interests and mine contents on social media|
|Data di pubblicazione:||2015|
|Appare nelle tipologie:||1.1 Articolo in rivista|