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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:

• Collaborative filtering
• Content-based filtering
• Hybrid recommender systems

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:

• Collaborative Filtering Recommender Systems (2011)
• Recommender Systems Handbook (2010)

See also my quick and dirty webpages:

• 100 Best Recommender System Videos | Meta-Guide.com
• Conversational Recommender Systems 2014 | Meta-Guide.com
• Recommender Dialog Systems 2013 | Meta-Guide.com
• Recommender Dialog Systems 2014 | Meta-Guide.com