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Heterogeneous hypergraph embedding for document recommendation.

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Title: Heterogeneous hypergraph embedding for document recommendation.
Authors: Zhu, Yu1 zy11120085@gmail.com, Guan, Ziyu2 welbyhebei@gmail.com, Tan, Shulong3 laos1984@gmail.com, Liu, Haifeng4 haifengliu@zju.edu.cn, Cai, Deng1 dengcai@cad.zju.edu.cn, He, Xiaofei1 xiaofeihe@gmail.com
Source: Neurocomputing. Dec2016, Vol. 216, p150-162. 13p.
Subjects: Fuzzy hypergraphs, Tags (Metadata), Object linking & embedding, Information services, Bibliography, Bookmarks (Websites), Information retrieval, Computer network resources
Abstract: Nowadays, more and more users are using online tagging services to organize their resources, e.g. Web bookmarks and bibliographies. Tags not only facilitate organization and retrieval of resources, but also provide valuable semantic descriptions for both resources and users’ interests. This work is focused on document recommendation using tagging data. Previous works either model the 3-order relation < user , tag , document > in tagging data by an ordinary graph or model different types of relations by a homogeneous hypergraph. The former scheme would lead to serious information loss, and the latter one fails to discern the influence of different types of relations. In this paper, we propose a heterogeneous hypergraph model which fully exploits high-order relational information in tagging data and, meanwhile, customizes the influence of different types of relations. A novel heterogeneous hypergraph embedding framework is developed for document recommendation. The framework is general and can incorporate various relations among users, tags and resources. Experimental results on two real-world datasets show the superiority of the proposed method over traditional methods. [ABSTRACT FROM AUTHOR]
Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Nowadays, more and more users are using online tagging services to organize their resources, e.g. Web bookmarks and bibliographies. Tags not only facilitate organization and retrieval of resources, but also provide valuable semantic descriptions for both resources and users’ interests. This work is focused on document recommendation using tagging data. Previous works either model the 3-order relation &lt; user , tag , document &gt; in tagging data by an ordinary graph or model different types of relations by a homogeneous hypergraph. The former scheme would lead to serious information loss, and the latter one fails to discern the influence of different types of relations. In this paper, we propose a heterogeneous hypergraph model which fully exploits high-order relational information in tagging data and, meanwhile, customizes the influence of different types of relations. A novel heterogeneous hypergraph embedding framework is developed for document recommendation. The framework is general and can incorporate various relations among users, tags and resources. Experimental results on two real-world datasets show the superiority of the proposed method over traditional methods. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=119096324
RecordInfo BibRecord:
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        Value: 10.1016/j.neucom.2016.07.030
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Tags (Metadata)
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      – SubjectFull: Object linking & embedding
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      – SubjectFull: Information services
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      – TitleFull: Heterogeneous hypergraph embedding for document recommendation.
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            NameFull: Zhu, Yu
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              M: 12
              Text: Dec2016
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