基于聚类的内容推荐的相关性、多样性和偶然性

Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018) Pub Date : 2018-10-22 DOI:10.5753/ENIAC.2018.4463

Fernando Costa, Andrei Martins Silva, S. M. Peres

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引用次数: 0

摘要

本文通过定义和分析以相关性、多样性和偶然性为目标的推荐策略，探讨了基于内容的推荐系统中的过度专业化问题。聚类是构建这些策略的基础，应用于新闻语境。结果表明了所提策略的可行性。

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Relevance, diversity and serendipity in content recommendation using clustering

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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Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018)

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