Editing Recommender system (section)

=== Hybrid recommendations approaches ===

Most recommender systems now use a hybrid approach, combining [[collaborative filtering]], content-based filtering, and other approaches. There is no reason why several different techniques of the same type could not be hybridized. Hybrid approaches can be implemented in several ways: by making content-based and collaborative-based predictions separately and then combining them; by adding content-based capabilities to a collaborative-based approach (and vice versa); or by unifying the approaches into one model.<ref name="Toward the Next Generation of Recommender Systems" /> Several studies that empirically compared the performance of the hybrid with the pure collaborative and content-based methods and demonstrated that the hybrid methods can provide more accurate recommendations than pure approaches. These methods can also be used to overcome some of the common problems in recommender systems such as cold start and the sparsity problem, as well as the knowledge engineering bottleneck in [[Knowledge base|knowledge-based]] approaches.<ref>Rinke Hoekstra, [http://www.semantic-web-journal.net/sites/default/files/swj32.pdf The Knowledge Reengineering Bottleneck], Semantic Web – Interoperability, Usability, Applicability 1 (2010) 1, IOS Press</ref>

[[Netflix]] is a good example of the use of hybrid recommender systems.<ref>{{cite journal|last1=Gomez-Uribe|first1=Carlos A.|last2=Hunt|first2=Neil|title=The Netflix Recommender System|journal=ACM Transactions on Management Information Systems|date=28 December 2015|volume=6|issue=4|pages=1–19|doi=10.1145/2843948|doi-access=free}}</ref> The website makes 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 highly (content-based filtering).

Some hybridization techniques include:
*'''Weighted''': Combining the score of different recommendation components numerically.
*'''Switching''': Choosing among recommendation components and applying the selected one.
*'''Mixed''': Recommendations from different recommenders are presented together to give the recommendation.
*'''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.<ref name=hybrids>Robin Burke, [http://www.dcs.warwick.ac.uk/~acristea/courses/CS411/2010/Book%20-%20The%20Adaptive%20Web/HybridWebRecommenderSystems.pdf Hybrid Web Recommender Systems] {{Webarchive|url=https://web.archive.org/web/20140912085014/http://www.dcs.warwick.ac.uk/~acristea/courses/CS411/2010/Book%20-%20The%20Adaptive%20Web/HybridWebRecommenderSystems.pdf |date=2014-09-12 }}, pp. 377-408, The Adaptive Web, Peter Brusilovsky, Alfred Kobsa, Wolfgang Nejdl (Ed.), Lecture Notes in Computer Science, Springer-Verlag, Berlin, Germany, Lecture Notes in Computer Science, Vol. 4321, May 2007, 978-3-540-72078-2.</ref>