Relevance, diversity and serendipity in content recommendation using clustering

• Fernando Henrique da Silva Costa USP
• Andrei Martins Silva USP
• Sarajane Marques Peres USP

DOI: https://doi.org/10.5753/eniac.2018.4463

Resumo

In this paper, over-specialization in content-based recommender sys- tems is explored through the definition and analysis of recommendation strate- gies aiming at quality in terms of relevance, diversity and serendipity. Clustering is applied as the basis for building these strategies, applied to the news context. The results show the feasibility of the proposed strategies.

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