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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Lahlou, F.Z., Benbrahim, H., Mountassir, A., Kassou, I. (2013). Inferring Context from Users’ Reviews for Context Aware Recommendation. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXX. SGAI 2013. Springer, Cham. https://doi.org/10.1007/978-3-319-02621-3_16
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DOI: https://doi.org/10.1007/978-3-319-02621-3_16
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