https://scholars.lib.ntu.edu.tw/handle/123456789/413157
標題: | Efficient and effective prediction of social tags to enhance web search | 作者: | Hsu M.-H. Chen H.-H. |
公開日期: | 2011 | 卷: | 62 | 期: | 8 | 起(迄)頁: | 1473-1487 | 來源出版物: | Journal of the American Society for Information Science and Technology | 摘要: | As the web has grown into an integral part of daily life, social annotation has become a popular manner for web users to manage resources. This method of management has many potential applications, but it is limited in applicability by the cold-start problem, especially for new resources on the web. In this article, we study automatic tag prediction for web pages comprehensively and utilize the predicted tags to improve search performance. First, we explore the stabilizing phenomenon of tag usage in a social bookmarking system. Then, we propose a two-stage tag prediction approach, which is efficient and is effective in making use of early annotations from users. In the first stage, content-based ranking, candidate tags are selected and ranked to generate an initial tag list. In the second stage, random-walk re-ranking, we adopt a random-walk model that utilizes tag co-occurrence information to re-rank the initial list. The experimental results show that our algorithm effectively proposes appropriate tags for target web pages. In addition, we present a framework to incorporate tag prediction in a general web search. The experimental results of the web search validate the hypothesis that the proposed framework significantly enhances the typical retrieval model. ? 2011 ASIS&T. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/413157 | ISSN: | 15322882 | DOI: | 10.1002/asi.21558 |
顯示於: | 資訊工程學系 |
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