Data-driven decision making in Critique-based recommenders: from a critique to social media data
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...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/195660 |
| Acceso en línea: | https://hdl.handle.net/2445/195660 |
| Access Level: | acceso abierto |
| Palabra clave: | Sistemes d'ajuda a la decisió Algorismes computacionals Mitjans socials Decision support systems Computer algorithms Social media |
| Sumario: | 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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