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An Introduction to Recommender Systems Read chapter Read first chapter

2016 | OriginalPaper | Chapter

6. Ensemble-Based and Hybrid Recommender Systems

Author : Charu C. Aggarwal

Published in: Recommender Systems

Publisher: Springer International Publishing

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Abstract

In the previous chapters, we discussed three different classes of recommendation methods. Collaborative methods use the ratings of a community of users in order to make recommendations, whereas content-based methods use the ratings of a single user in conjunction with attribute-centric item descriptions to make recommendations.

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previous chapter Knowledge-Based Recommender Systems
next chapter Evaluating Recommender Systems
Footnotes
1
Both entries were tied on the error rate. The award was given to the former because it was submitted 20 minutes earlier.
 
2
This is also referred to as a pipelined system [275].
 
3
It is possible for the unspecified values in duplicate rows to predicted differently, even though this is relatively unusual for most collaborative filtering algorithms.
 
4
The work in [67] proposes only the first technique for computing the similarity.
 
5
In the context of the Netflix Prize contest, this was achieved on a special part of the data set, referred to as the probe set. The probe set was not used for building the component ensemble models.
 
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Metadata
Title
Ensemble-Based and Hybrid Recommender Systems
Author
Charu C. Aggarwal
Copyright Year
2016
Book
Recommender Systems

Print ISBN: 978-3-319-29657-9

Electronic ISBN: 978-3-319-29659-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-29659-3_6

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