SERUM: Collecting Semantic User Behavior for Improved News Recommendations
SERUM: Collecting Semantic User Behavior for Improved News Recommendations
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SERUM：收集语义用户行为以改进新闻推荐
DOI：
10.1007/978-3-642-28509-7_37
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Sahin Albayrak
中科院分区：
文献类型：
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作者：
Till Plumbaum;Andreas Lommatzsch;Ernesto De Luca;Sahin Albayrak
如何将语义数据和语义技术用于个性化和推荐服务？本文提出了基于大型非结构化数据集的语义推荐系统SERVER，该系统利用语义技术来收集隐含的用户行为并建立语义用户模型。这些模型与大规模语义数据集相结合，然后使用基于图的算法来计算个性化新闻推荐。我们介绍了用于语义数据管理、个性化和推荐的血清构建块，重点介绍了隐式用户行为收集。因此，我们的系统使用RDFa来收集有意义的用户行为，并使用自主开发的用户行为本体(User Behavior Ontology，简称UBO)来构建语义用户行为模型。这项工作的主要贡献是介绍了UBO以及与之相关的语义用户跟踪和建模过程。
How can semantic data and semantic technologies be leveraged for personalization and recommendation services? In this paper, we present SERUM (Semantic Recommendations based on large unstructured datasets), a news recommendation system that utilizes semantic technologies to collect implicit user behavior and to build semantic user models. These models, combined with large-scale semantic datasets, are then used to compute personalized news recommendations using graph-based algorithms. We introduce the building blocks of SERUM for the semantic data management, personalization and recommendation, with the main focus on the implicit user behavior collection. Therefore, our system uses RDFa to collect meaningful user behavior and a self-developed user behavior ontology (the User Behavior Ontology, in short UBO) to build semantic user behavior models. The main contribution of this work is the introduction of the UBO and the associated semantic user tracking and modeling process.
DOI：
10.1007/978-3-642-38844-6
发表时间：
2013
期刊：
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影响因子：
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作者：
V. Dimitrova;T. Kuflik;David N. Chin;F. Ricci;Peter Dolog;G. Houben
通讯作者：
V. Dimitrova;T. Kuflik;David N. Chin;F. Ricci;Peter Dolog;G. Houben
用语义丰富 Ajax：Web 个性化用例
影响因子：
10
作者：
Kay;Ljiljana Stojanović;N. Stojanović;S. Thomas
通讯作者：
S. Thomas