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Recommendation Techniques on a Knowledge Graph for Email Remarketing

机译:关于电子邮件再营销的知识图表的推荐技巧

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The knowledge graph, which is an ontology based representation technique, is described to model the information necessary to conduct collaborative filtering, content-based filtering and knowledge based recommendation methods. Spreading activation and network science based recommendation methods are presented and evaluated. The evaluation measures are calculated on top list recommendations, where rating estimation is not necessary. In the experiment, click-through rates are measured and presented based on the email based remarketing activity of an electronic commerce system. Our primary result shows the improved recommendation quality of spreading activation based methods compared to the human expert.
机译:描述是基于本体的表示技术的知识图来描述进行进行协作滤波,基于内容的过滤和基于知识的推荐方法所需的信息。提出和评估了扩展激活和网络科学的推荐方法。评估措施按顶部列表建议计算,其中不需要评级估算。在实验中,基于电子商务系统的基于电子邮件的电子邮件的再营销活动来测量和呈现点击率。我们的主要结果显示了与人类专家相比扩散激活的方法的提高建议质量。

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