推荐系统的一种启发式方法

A. Werner-Stark, Z. Nagy
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引用次数: 2

摘要

推荐系统是信息过滤系统的一个子类,它试图预测哪些项目最可能引起用户的兴趣。基于内容的推荐系统会推荐与之前喜欢的项目相似的项目。上下文感知推荐系统是一种基于内容的推荐系统,其中条目根据其属性值进行过滤。本文采用启发式方法建立了上下文感知推荐系统的通用数学模型。该方法被实现为一个多用户演示应用程序,一个电影推荐系统。对应用程序进行了测试,并使用不同的度量对结果进行了评估。可以说,所开发的方法比现有的方法更精确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Heuristic Method to Recommendation Systems
Recommendation systems are a subclass of information filtering systems that seek to predict which items are most likely interesting to the user. Content-based recommendation systems recommend items that are similar to the previously liked items. Context-aware recommendation systems are a type of content-based recommendation systems, where the items are filtered based on the value of their attributes. In this paper, a general mathematical model was developed for context-aware recommendation systems using a heuristic method. This method was implemented as a multiuser demo application, a movie recommendation system. The application was tested and the results have been evaluated with different metrics. It could be declared that the developed method is more precise than the existing methods.
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