Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

DOI: http://dx.doi.org/10.15439/2024F9868

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 225–230 (2024)

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Abstract. 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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