Multi-stakeholder News Recommendation Using Hypergraph Learning

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Abstract

Recommender systems are meant to fulfil user preferences. Nevertheless, there are multiple examples where users are not the only stakeholder in a recommendation platform. For instance, in news aggregator websites apart from readers, one can consider magazines (news agencies) or authors as other stakeholders. A multi-stakeholder recommender system generates a ranked list of items taking into account the preferences of multiple stakeholders. In this study, news recommendation is handled as a hypergraph ranking task, where relations between multiple types of objects and stakeholders are modeled in a unified hypergraph. The obtained results indicate that ranking on hypergraphs can be utilized as a natural multi-stakeholder recommender system that is able to adapt recommendations based on the importance of stakeholders.

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Gharahighehi, A., Vens, C., & Pliakos, K. (2020). Multi-stakeholder News Recommendation Using Hypergraph Learning. In Communications in Computer and Information Science (Vol. 1323, pp. 531–535). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-65965-3_36

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