Recommender systems have become extremely common in recent years, and are applied in a variety of applications. The most popular ones are probably movies, music, news, books, research articles, search queries, social tags, and products in general. However, there are
Collective intelligence is shared or group intelligence that emerges from the collaboration, collective efforts, and competition of many individuals and appears in consensus decision making. The term appears in sociobiology, political science and in context of mass peer review and
An Information filtering system is a system that removes redundant or unwanted information from an information stream using (semi)automated or computerized methods prior to presentation to a human user. Its main goal is the management of the information overload and
A filter bubble is a result state in which a website algorithm selectively guesses what information a user would like to see based on information about the user (such as location, past click behaviour and search history) and, as a
Cold start is a potential problem in computer-based information systems which involve a degree of automated data modelling. Specifically, it concerns the issue that the system cannot draw any inferences for users or items about which it has not yet
Multi-Criteria Recommender Systems (MCRS) can be defined as Recommender Systems that incorporate preference information upon multiple criteria. Instead of developing recommendation techniques based on a single criterion values, the overall preference of user u for the item i, these systems
Diversity – Users tend to be more satisfied with recommendations when there is a higher intra-list diversity, i.e. items from e.g. different artists. Recommender Persistence – In some situations it is more effective to re-show recommendations, or let users re-rate
Netflix is a good example of hybrid systems. They make recommendations by comparing the watching and searching habits of similar users (i.e. collaborative filtering) as well as by offering movies that share characteristics with films that a user has rated
When building a model from a user’s profile, a distinction is often made between explicit and implicit forms of data collection. Examples of explicit data collection include the following: Asking a user to rate an item on a sliding scale.
To abstract the features of the items in the system, item presentation algorithm is applied. A widely used algorithm is the tf–idf representation (also called vector space representation).