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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/195660
Data-driven decision making in Critique-based recommenders: from a critique to social media data
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In the last decade there have been a large number of proposals in the field of Critique-based Recommenders. Critique-based recommenders are data-driven in their nature sincethey use a conversational cyclical recommendation process to elicit user feedback. In theliterature, the proposals made differ mainly in two aspects: in the source of data and in howthis data is analyzed to extract knowledge for providing users with recommendations. Inthis paper, we propose new algorithms that address these two aspects. Firstly, we propose anew algorithm, called HOR, which integrates several data sources, such as current user pref-erences (i.e., a critique), product descriptions, previous critiquing sessions by other users,and users' opinions expressed as ratings on social media web sites. Secondly, we propose adding compatibility and weighting scores to turn user behavior into knowledge to HOR and a previous state-of-the-art approach named HGR to help both algorithms make smarter recommendations. We have evaluated our proposals in two ways: with a simulator and withreal users. A comparison of our proposals with state-of-the-art approaches shows that thenew recommendation algorithms significantly outperform previous ones.
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CONTRERAS, David and SALAMÓ LLORENTE, Maria. Data-driven decision making in Critique-based recommenders: from a critique to social media data. Journal of Intelligent Information Systems. 2020. Vol. 54, num. 23-44. ISSN 0925-9902. [consulted: 6 of October of 2026]. Available at: https://hdl.handle.net/2445/195660