HeteRecom: a semantic-based recommendation system in heterogeneous networks
HeteRecom: a semantic-based recommendation system in heterogeneous networks
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DOI：
10.1145/2339530.2339778
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发表时间：
2012-08
期刊：
影响因子：
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通讯作者：
C. Shi;Chong Zhou;Xiangnan Kong;Philip S. Yu;Gang Liu;Bai Wang
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文献类型：
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作者：
C. Shi;Chong Zhou;Xiangnan Kong;Philip S. Yu;Gang Liu;Bai Wang
随着WWW的快速发展，为用户提供准确的推荐已经成为电子商务系统的一项重要功能。传统的推荐系统通常推荐与查询对象类型相同的相似对象，而不探索不同相似度量的语义。本文将推荐系统中的对象组织成一个异构网络。通过使用基于路径的关联度量来评估任意类型对象之间的相关性，并捕获每个路径中包含的微妙语义，我们实现了一个基于语义推荐的原型系统（称为HeteRecom）。HeteRecom具有以下独特的属性：(1)根据用户指定的路径提供基于语义的推荐功能。(2)推荐同类型的相似对象和不同类型的相关对象。我们用一个真实世界的电影数据集证明了我们系统的有效性。
Making accurate recommendations for users has become an important function of e-commerce system with the rapid growth of WWW. Conventional recommendation systems usually recommend similar objects, which are of the same type with the query object without exploring the semantics of different similarity measures. In this paper, we organize objects in the recommendation system as a heterogeneous network. Through employing a path-based relevance measure to evaluate the relatedness between any-typed objects and capture the subtle semantic containing in each path, we implement a prototype system (called HeteRecom) for semantic based recommendation. HeteRecom has the following unique properties: (1) It provides the semantic-based recommendation function according to the path specified by users. (2) It recommends the similar objects of the same type as well as related objects of different types. We demonstrate the effectiveness of our system with a real-world movie data set.