In recommender systems, what are the mechanisms that result in recommendations?


In recommender systems, what are the mechanisms that result in recommendations?

According to the Wikipedia entry on Recommender system, there are three main approaches, including:

Hybrid recommender systems combine multiple techniques to achieve synergy, such as:

  • Weighted: The score of different recommendation components are combined numerically.
  • Switching: The system chooses among recommendation components and applies the selected one.
  • Mixed: Recommendations from different recommenders are presented together.
  • Feature Combination: Features derived from different knowledge sources are combined together and given to a single recommendation algorithm.
  • Feature Augmentation: One recommendation technique is used to compute a feature or set of features, which is then part of the input to the next technique.
  • Cascade: Recommenders are given strict priority, with the lower priority ones breaking ties in the scoring of the higher ones.
  • Meta-level: One recommendation technique is applied and produces some sort of model, which is then the input used by the next technique.

References:

See also my quick and dirty webpages:

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