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首页> 外文期刊>International journal of cognitive informatics and natural intelligence >Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks
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Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

机译：在在线社交网络中为个性化推荐系统进行增强基于上下文的概念用户建模

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摘要

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.
机译：随着在线社交网络上的数据越来越大，构建向用户推荐适当内容的个性化推荐系统非常重要，在此字段中有很多研究，它使用文本的概念表示来匹配具有最佳内容的用户模型。本文提出了一种新的方法来构建用户模型，其通过使用超出命名实体的概念概念来包括单词和短语的常见意义概念来依赖于文本的概念表示。该模型包括概念的上下文信息，也显示了一种新颖的方法来利用知识库的语义关系来扩展用户模型，实验表明所提出的模型和相关的推荐算法优于详细的所有先前方法比较在本文中显示。

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