Literature Books Recommender System using Collaborative Filtering and Multi-Source Reviews

In this contribution, we present a method for obtaining literature books recommendations using collaborative filtering recommender system technique and emotions extracted from multi-source online reviews. We experimentally validated the proposed system using a book dataset and associated reviews that we collected from Goodreads and Amazon websites using our customized web scrapers. We show the benefits of using multi-source reviews by proposing a series of recommender system evaluation measures, which include single-source and multi-source recommendations similarity, recommendation algorithm usecases coverage and generated recommendations relevance.

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1. Main Track: Short Papers

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2. Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).