Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems

Joel Pinho Lucas; Saddys Segrera; Maria N. Moreno

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Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems

机译：利用关联分类器以减轻推荐器系统中的典型缺陷

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摘要

Nowadays, there is a constant need for personalization in e-commerce systems. Recommender systems make suggestions and provide information about items available, however, many recommender techniques are still vulnerable to some shortcomings. In this work, we analyze how methods employed in these systems are affected by some typical drawbacks. Hence, we conduct a case study using data gathered from real recommender systems in order to investigate what machine learning methods can alleviate such drawbacks. Due to some especial features inherited by associative classifiers, we give a particular attention to this category of methods to test their capability of dealing with typical drawbacks.

机译：如今，电子商务系统中一直需要个性化。推荐系统提供建议并提供有关可用项目的信息，但是，许多推荐技术仍然容易受到某些缺点的影响。在这项工作中，我们分析了这些系统中采用的方法如何受到一些典型缺陷的影响。因此，我们使用从真实推荐系统收集的数据进行案例研究，以研究哪种机器学习方法可以缓解此类缺陷。由于关联分类器继承了一些特殊功能，因此我们特别注意此类方法，以测试其处理典型缺陷的能力。

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《Expert systems with applications》 |2012年第1期|p.1273-1283|共11页
作者
Joel Pinho Lucas; Saddys Segrera; Maria N. Moreno;
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作者单位

Departamento de Informdtica y Automatka, Universidad de Salamanca, Plaza de la Merced s 37008, Salamanca, Spain;

Departamento de Informdtica y Automatka, Universidad de Salamanca, Plaza de la Merced s 37008, Salamanca, Spain;

Departamento de Informdtica y Automatka, Universidad de Salamanca, Plaza de la Merced s 37008, Salamanca, Spain;

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正文语种 eng
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recommender systems; associative classification; sparsity;

机译：推荐系统;关联分类稀疏性;
入库时间 2022-08-17 13:31:49

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5. Associative Recommender System for Protein Crystallization Screening. [D] . Juttu, Mahesh Kumar. 2017

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Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems

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