THRON's artificial intelligence leverages a set of algorithms for processing data on how content are used; it can correlate visitors, content, access frequency and other relevant data.
The main goal is to transform collected data into visualizations thus generating insights, trends and other important information.
THRON users and content can be categorized and enriched by tags.
Thanks to its artificial intelligence, THRON platform is constantly monitoring access to content thus automatically improving their classification, according to their actual use: comparing content classification to users interests. At the same time it will allow a better user classification, comparing their interest to the topics of their most visited content.
This mechanism will make you always capable of proposing content which are relevant for specific users.
Each asset is automatically enriched by THRON semantic analysis and image recognition with tags that describe the concepts and object that are present in the asset.
THRON Behavior engine processes several user access parameters including content view duration, content view frequency and recency. Behavior engine works at concept level so it extracts, among the other values, how much time a user spent on a specific topic, how often he engages with that topic and how recent was his interest towards such topic. Based on those values it enriches user profile (works also on anonymous users) by adding topic of interest as tags.
- The engine will remove ONLY tags managed by the engine. Tags that have been added manually will never be removed by the engine
- Tags which have been automatically added by the engine cannot be manually removed (because they are based on real analytics)
Below is an infographic that will help you understand the functioning of the behavior engine.
Let's suppose you have two profiled users, a woman and a man, and two content, an image and a video. These four entities have already been tagged with the following set:
As soon as the two content are embedded in your websites, your users start watching them, in particular let's assume that the woman looks at the image content, since it matches with her interest, while the man looks at the video content for the same reason.
Over time, Behavior engine will process these information and, if visits to content performed by these users exceed the configuration threshold, it will assign to content the tags of the TARGET class comparing them with those of the users accessing such content.
Similarly, a feedback process will be carried out: if the visits to content performed by users exceed the configuration threshold, Behavior engine will add tags in the users' TOPIC class, comparing them with those of the content they viewed.