Community-Aware Resource Profiling for Personalized Search in Folksonomy
In recent years, there is a fast proliferation of collaborative tagging (a.k.a. folksonomy) systems in Web 2.0 communities. With the increasingly large amount of data, how to assist users in searching their interested resources by utilizing these semantic tags becomes a crucial problem. Collaborativ...
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Veröffentlicht in: | Journal of computer science and technology 2012, Vol.27 (3), p.599-610 |
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description | In recent years, there is a fast proliferation of collaborative tagging (a.k.a. folksonomy) systems in Web 2.0 communities. With the increasingly large amount of data, how to assist users in searching their interested resources by utilizing these semantic tags becomes a crucial problem. Collaborative tagging systems provide an environment for users to annotate resources, and most users give annotations according to their perspectives or feelings. However, users may have different perspectives or feelings on resources, e.g., some of them may share similar perspectives yet have a conflict with others. Thus, modeling the profile of a resource based on tags given by all users who have annotated the resource is neither suitable nor reasonable. We propose, to tackle this problem in this paper, a community-aware approach to constructing resource profiles via social filtering. In order to discover user communities, three different strategies are devised and discussed. Moreover, we present a personalized search approach by combining a switching fusion method and a revised needs-relevance function, to optimize personalized resources ranking based on user preferences and user issued query. We conduct experiments on a collected real life dataset by comparing the performance of our proposed approach and baseline methods. The experimental results verify our observations and effectiveness of proposed method. |
doi_str_mv | 10.1007/s11390-012-1247-7 |
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With the increasingly large amount of data, how to assist users in searching their interested resources by utilizing these semantic tags becomes a crucial problem. Collaborative tagging systems provide an environment for users to annotate resources, and most users give annotations according to their perspectives or feelings. However, users may have different perspectives or feelings on resources, e.g., some of them may share similar perspectives yet have a conflict with others. Thus, modeling the profile of a resource based on tags given by all users who have annotated the resource is neither suitable nor reasonable. We propose, to tackle this problem in this paper, a community-aware approach to constructing resource profiles via social filtering. In order to discover user communities, three different strategies are devised and discussed. Moreover, we present a personalized search approach by combining a switching fusion method and a revised needs-relevance function, to optimize personalized resources ranking based on user preferences and user issued query. We conduct experiments on a collected real life dataset by comparing the performance of our proposed approach and baseline methods. 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In order to discover user communities, three different strategies are devised and discussed. Moreover, we present a personalized search approach by combining a switching fusion method and a revised needs-relevance function, to optimize personalized resources ranking based on user preferences and user issued query. We conduct experiments on a collected real life dataset by comparing the performance of our proposed approach and baseline methods. 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subjects | Annotations Artificial Intelligence Collaboration Communities Computer Science Customization Data Structures and Information Theory Experiments Filtering Grants Information Systems Applications (incl.Internet) Marking Mathematical models Personalized Recipes Regular Paper Searching Semantics Software Engineering Tagging Tags Theory of Computation User behavior Vocabularies & taxonomies Web 2.0 个性化搜索 分类法 标签系统 社会过滤 社区意识 网络系统 资源优化 配置文件 |
title | Community-Aware Resource Profiling for Personalized Search in Folksonomy |
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