Information filtering system
lastmod 2024-04-24created 2024-04-242 edits
An information filtering system is a system that removes redundant or unwanted information from an information stream using (semi-)automated or computerized methods before it is presented to a human user. Its main goal is to manage information overload and increase the semantic signal-to-noise ratio. To do this, the user's profile is compared with some reference characteristics. These characteristics may originate from the information item (the content-based approach) or the user's social environment (the collaborative filtering approach).
Whereas in information transmission signal processing, filters (signal processing) are used against noise that disrupts the context at the bit level, the methods used in information filtering operate at the semantic level.
The range of machine methods used is based on the same principles as information extraction. A notable application can be found in the field of email spam filters. Thus, it is not only the information explosion that requires some form of filter, but also inadvertently or maliciously introduced pseudo-information.
At the presentation level, information filtering takes the form of news feeds based on user preferences, etc.
Recommender systems and content discovery platforms are active information filtering systems that attempt to present to the user information items (movies, television, music, books, news, web pages) the user is interested in. These systems add information items to the information flowing toward the user. As opposed to removing information items from the information flow toward the user, the user. Recommender systems typically use collaborative filtering approaches or a combination of collaborative filtering and content-based filtering approaches, but content-based recommender systems do exist.
Categories
See also
- AI
- Machine Learning
- Filter bubble
- Korean: 칼만 필터