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
Hybrid Recommendation Systems
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
Collaborative filtering
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.
Content-based filtering
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).
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