Relevance, diversity and serendipity in content recommendation using clustering - Costa, Fernando Henrique da Silva; Silva, Andrei Martins; Peres, Sarajane Marques (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2018: Anais do XV Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2018.4463, 2018)
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.