| 書目名稱 | Recommender Systems for Social Tagging Systems | | 編輯 | Leandro Balby Marinho,Andreas Hotho,Panagiotis Sym | | 視頻video | http://file.papertrans.cn/825/824130/824130.mp4 | | 概述 | Includes supplementary material: | | 叢書名稱 | SpringerBriefs in Electrical and Computer Engineering | | 圖書封面 |  | | 描述 | Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new | | 出版日期 | Book 2012 | | 關鍵詞 | Folksonomy; Multimode Recommendations; Recommender Systems; Social Tagging; Tag-Aware Recommendations | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4614-1894-8 | | isbn_softcover | 978-1-4614-1893-1 | | isbn_ebook | 978-1-4614-1894-8Series ISSN 2191-8112 Series E-ISSN 2191-8120 | | issn_series | 2191-8112 | | copyright | The Author(s) 2012 |
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