https://thenextweb.com/neural/2021/01/03/how-ai-weeds-the-spam-out-of-our-inboxes-syndication/
The one thing they have in common is that they are irrelevant to the needs of the recipient. A spam-detector algorithm must find a way to filter out spam while and at the same time avoid flagging authentic messages that users want to see in their inbox.
Static rules can help.
But for the most part, spam detection mainly relies on analyzing the content of the message.
Different machine learning algorithms can detect spam, but one that has gained appeal is the “naïve Bayes” algorithm.
Spam detection is a supervised machine learning problem. This means you must provide your machine learning model with a set of examples of spam and ham messages and let it find the relevant patterns that separate the two different categories.
But this does not mean that it is perfect.
A final thing to note is that spam detection is always a work in progress. As developers use AI and other technology to detect and filter out noisome messages from emails, spammers find new ways to game the system and get their junk past the filters. That is why email providers always rely on the help of users to improve and update their spam detectors.
criteria : N. 표준, 기준
