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Natural Language Processing for Recommender Systems

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Recommender Systems Handbook

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.

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Notes

  1. 1.

    https://www.yelp.com/dataset-challenge.

  2. 2.

    http://jmcauley.ucsd.edu/data/amazon.

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Authors and Affiliations

  1. Facebook, Tel Aviv-Yafo, Israel

    Oren Sar Shalom

  2. Buyer Experience Research Department, eBay Research, Netanya, Israel

    Haggai Roitman

  3. Data Science Department, Relational AI, Berkeley, CA, USA

    Pigi Kouki

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  1. Oren Sar Shalom
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  2. Haggai Roitman
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  3. Pigi Kouki
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Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

  2. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Shalom, O.S., Roitman, H., Kouki, P. (2022). Natural Language Processing for Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2197-4_12

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