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Recommender Systems

Recommender systems

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

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Multi-criteria Recommender Systems

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

admin 8 May, 201411 March, 2015 Recommender Systems Read more

Beyond Accuracy

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

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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

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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.

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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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