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Erschienen in:
An Introduction to Recommender Systems Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

4. Content-Based Recommender Systems

verfasst von : Charu C. Aggarwal

Erschienen in: Recommender Systems

Verlag: Springer International Publishing

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Abstract

The collaborative systems discussed in the previous chapters use the correlations in the ratings patterns across users to make recommendations. On the other hand, these methods do not use item attributes for computing predictions. This would seem rather wasteful; after all, if John likes the futuristic science fiction movie Terminator, then there is a very good chance that he might like a movie from a similar genre, such as Aliens. In such cases, the ratings of other users may not be required to make meaningful recommendations.

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Vorheriges Kapitel Model-Based Collaborative Filtering
Nächstes Kapitel Knowledge-Based Recommender Systems
Fußnoten
1
The exact recommendation method used by IMDb is proprietary and not known to the author. The description here is intended only for illustrative purposes.
 
2
For structured data, the centroid of the group may be used.
 
3
A different approach in collaborative filtering is to leverage user-user rules. For user-user rules, the antecedents and consequents may both contain the ratings of specific users. Refer to section 3.​3 of Chapter 3.
 
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Metadaten
Titel
Content-Based Recommender Systems
verfasst von
Charu C. Aggarwal
Copyright-Jahr
2016
Buch
Recommender Systems

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

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

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-29659-3_4

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