Collaborative filtering in social tagging systems based on joint item-tag recommendations
J. Peng, D. Zeng, H. Zhao, und F. Wang. Proceedings of the 19th ACM international conference on Information and knowledge management, Seite 809--818. New York, NY, USA, ACM, (2010)
DOI: 10.1145/1871437.1871541
Zusammenfassung
Tapping into the wisdom of the crowd, social tagging can be considered an alternative mechanism - as opposed to Web search - for organizing and discovering information on the Web. Effective tag-based recommendation of information items, such as Web resources, is a critical aspect of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based recommendation method. While most of the existing research either implicitly or explicitly assumes a simple tripartite graph structure for this purpose, we propose a comprehensive information structure to capture all types of co-occurrence information in the tagging data. Based on the proposed information structure, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by our proposed user profile, we propose a novel framework for collaborative filtering in social tagging systems. In our proposed framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the <i>wisdom from the crowd</i> and projected to the item space for final item recommendations. Evaluation using three real-world datasets shows that our proposed recommendation approach significantly outperformed state-of-the-art approaches.
Beschreibung
Collaborative filtering in social tagging systems based on joint item-tag recommendations
%0 Conference Paper
%1 peng2010collaborative
%A Peng, Jing
%A Zeng, Daniel Dajun
%A Zhao, Huimin
%A Wang, Fei-yue
%B Proceedings of the 19th ACM international conference on Information and knowledge management
%C New York, NY, USA
%D 2010
%I ACM
%K Collaborative-Filtering joint-recommendation social-information-access social-tagging tagging-structure
%P 809--818
%R 10.1145/1871437.1871541
%T Collaborative filtering in social tagging systems based on joint item-tag recommendations
%U http://doi.acm.org/10.1145/1871437.1871541
%X Tapping into the wisdom of the crowd, social tagging can be considered an alternative mechanism - as opposed to Web search - for organizing and discovering information on the Web. Effective tag-based recommendation of information items, such as Web resources, is a critical aspect of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based recommendation method. While most of the existing research either implicitly or explicitly assumes a simple tripartite graph structure for this purpose, we propose a comprehensive information structure to capture all types of co-occurrence information in the tagging data. Based on the proposed information structure, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by our proposed user profile, we propose a novel framework for collaborative filtering in social tagging systems. In our proposed framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the <i>wisdom from the crowd</i> and projected to the item space for final item recommendations. Evaluation using three real-world datasets shows that our proposed recommendation approach significantly outperformed state-of-the-art approaches.
%@ 978-1-4503-0099-5
@inproceedings{peng2010collaborative,
abstract = {Tapping into the wisdom of the crowd, social tagging can be considered an alternative mechanism - as opposed to Web search - for organizing and discovering information on the Web. Effective tag-based recommendation of information items, such as Web resources, is a critical aspect of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based recommendation method. While most of the existing research either implicitly or explicitly assumes a simple tripartite graph structure for this purpose, we propose a comprehensive information structure to capture all types of co-occurrence information in the tagging data. Based on the proposed information structure, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by our proposed user profile, we propose a novel framework for collaborative filtering in social tagging systems. In our proposed framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the <i>wisdom from the crowd</i> and projected to the item space for final item recommendations. Evaluation using three real-world datasets shows that our proposed recommendation approach significantly outperformed state-of-the-art approaches.},
acmid = {1871541},
added-at = {2017-03-13T05:16:30.000+0100},
address = {New York, NY, USA},
author = {Peng, Jing and Zeng, Daniel Dajun and Zhao, Huimin and Wang, Fei-yue},
biburl = {https://www.bibsonomy.org/bibtex/225cf7dafa4391dd064e5a08c88f9a9b0/hungchau},
booktitle = {Proceedings of the 19th ACM international conference on Information and knowledge management},
description = {Collaborative filtering in social tagging systems based on joint item-tag recommendations},
doi = {10.1145/1871437.1871541},
interhash = {08dd8815199a061d889a4b64ff380dc2},
intrahash = {25cf7dafa4391dd064e5a08c88f9a9b0},
isbn = {978-1-4503-0099-5},
keywords = {Collaborative-Filtering joint-recommendation social-information-access social-tagging tagging-structure},
location = {Toronto, ON, Canada},
numpages = {10},
pages = {809--818},
publisher = {ACM},
series = {CIKM '10},
timestamp = {2017-04-12T19:15:26.000+0200},
title = {Collaborative filtering in social tagging systems based on joint item-tag recommendations},
url = {http://doi.acm.org/10.1145/1871437.1871541},
year = 2010
}