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2016 | OriginalPaper | Buchkapitel

12. Attack-Resistant Recommender Systems

verfasst von : Charu C. Aggarwal

Erschienen in: Recommender Systems

Verlag: Springer International Publishing

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Abstract

The input to recommender systems is typically provided through open platforms. Almost anyone can register and submit a review at sites such as Amazon.com and Epinions.com. Like any other data-mining system, the effectiveness of a recommender system depends almost exclusively on the quality of the data available to it. Unfortunately, there are significant motivations for participants to submit incorrect feedback about items for personal gain or for malicious reasons:

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Vorheriges Kapitel Social and Trust-Centric Recommender Systems

Nächstes Kapitel Advanced Topics in Recommender Systems

The ROC curve is used in a different context here than in Chapter 7 In Chapter 7, the ROC curve measures the effectiveness of ranking items for recommendations. Here, we measure the effectiveness of ranking user profiles based on their likelihood of being fake. However, the general principle of using the ROC curve is similar in both cases, because a ranking is compared with the binary ground-truth in both cases.

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Titel: Attack-Resistant Recommender Systems
verfasst von: Charu C. Aggarwal
Verlag: Springer International Publishing
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_12

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