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Pattern Graphs: Combining Multivariate Time Series and Labelled Interval Sequences for Classification Read chapter Read first chapter

2013 | OriginalPaper | Chapter

Inferring Context from Users’ Reviews for Context Aware Recommendation

Authors : F. Z. Lahlou, H. Benbrahim, A. Mountassir, I. Kassou

Published in: Research and Development in Intelligent Systems XXX

Publisher: Springer International Publishing

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Abstract

Context Aware Recommendation Systems are Recommender Systems that provide recommendations based not only on users and items, but also on other information related to the context. A first challenge in building these systems is to obtain the contextual information. In this paper, we explore how accurate it is possible to infer contextual information from users’ reviews. For this purpose, we use Text Classification techniques and conduct several experiments to identify the appropriate Text Representation settings and classification algorithm to the context inference problem. We carry out our experiments on two datasets containing reviews related to hotels and cars, and aim to infer the contextual information ‘intent of purchase’ from these reviews. To infer context from reviews, we recommend removing terms that occur once in the data set, combining unigrams, bigrams and trigrams, adopting a TFIDF weighting schema and using the Multinomial algorithm rather Naïve Bayes than Support Vector Machines.

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Footnotes
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Metadata
Title
Inferring Context from Users’ Reviews for Context Aware Recommendation
Authors
F. Z. Lahlou
H. Benbrahim
A. Mountassir
I. Kassou
Copyright Year
2013
Book
Research and Development in Intelligent Systems XXX

Print ISBN: 978-3-319-02620-6

Electronic ISBN: 978-3-319-02621-3

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-319-02621-3_16

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