Personalized search for social media via dominating verbal context

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• 作者：Haoran Xie^aAuthor Vitae ; Xiaodong Li^bAuthor Vitae ; Tao Wang^cAuthor Vitae ; Li Chen^dAuthor Vitae ; Ke Li^bAuthor Vitae ; Fu Lee Wang^aAuthor Vitae ; Yi Cai^c ; Author Vitae ; Qing Li^b ; ^eAuthor Vitae ; Huaqing Min^cAuthor Vitae
• 关键词：Dominating set ; Context ; Folksonomy ; Personalized search ; Collaborative tagging
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：8 January 2016
• 年：2016
• 卷：172
• 期：Complete
• 页码：27-37
• 全文大小：1133 K

文摘

With the rapid development of Web 2.0 communities, there has been a tremendous increase in user-generated content. Confronting such a vast volume of resources in collaborative tagging systems, users require a novel method for fast exploring and indexing so as to find their desired data. To this end, contextual information is indispensable and critical in understanding user preferences and intentions. In sociolinguistics, context can be classified as verbal context and social context. Compared with verbal context, social context requires not only domain knowledge to build pre-defined contextual attributes but also additional user data. However, to the best of our knowledge, no research has addressed the issue of irrelevant contextual factors for the verbal context model. To bridge this gap, the dominating set obtained from verbal context is proposed in this paper. We present (i) the verbal context graph to model contents and interrelationships of verbal context in folksonomy and thus capture the user intention; (ii) a method of discovering dominating set that provides a good balance of essentiality and integrality to de-emphasize irrelevant contextual factors and to keep the major characteristics of the verbal context graph; and (iii) a revised ranking method for measuring the relevance of a resource to an issued query, a discovered context and an extracted user profile. The experimental results obtained for a public dataset illustrate that the proposed method is more effective than existing baseline approaches.