Natural Language Processing for Recommender Systems

Oren Sar Shalom, Haggai Roitman, Pigi Kouki

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Scopus citations

Abstract

In the race for improved modeling, modern recommender systems enhance collaborative filtering (CF) by using diverse signals that provide additional information on users’ preferences or items’ traits. Arguably, the most meaningful signal for recommenders is textual data, which includes examples like user-generated reviews, textual-item descriptions and even conversational interaction in natural language. Additionally, the output of a typical recommender may include free-form text as well, when auto generated explanations are associated with the suggested items. In this chapter, we describe cases where Natural Language Processing (NLP) can aid recommender systems. We first identify the possible tangent points between NLP and recommenders. Next, we present systems that successfully exploit the interaction between these two fields. Finally, for each such case we indicate its relative advantages and limitations.

Original languageEnglish
Title of host publicationRecommender Systems Handbook
Subtitle of host publicationThird Edition
PublisherSpringer US
Pages447-483
Number of pages37
ISBN (Electronic)9781071621974
ISBN (Print)9781071621967
DOIs
StatePublished - 1 Jan 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© Springer Science+Business Media, LLC, part of Springer Nature 2011, 2015, 2022.

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