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
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).