Improving recommendation based on features' co-occurrence effects in collaborative tagging systems

Hao Han; Yi Cai; Yifeng Shao; Qing Li

doi:10.1007/978-3-642-29253-8_61

Improving recommendation based on features' co-occurrence effects in collaborative tagging systems

Hao Han, Yi Cai, Yifeng Shao, Qing Li

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

7 Citations (Scopus)

Abstract

Currently, recommender system becomes more and more important and challenging, as users demand higher recommendation quality. Collaborative tagging systems allow users to annotate resources with their own tags which can reflect users' attitude on these resources and some attributes of resources. Based on our observation, we notice that there is co-occurrence effect of features, which may cause the change of user's favor on resources. Current recommendation methods do not take it into consideration. In this paper, we propose an assistant and enhanced method to improve the performance of other methods by combining co-occurrence effect of features in collaborative tagging environment. © 2012 Springer-Verlag Berlin Heidelberg.

Original language	English
Title of host publication	Web Technologies and Applications
Subtitle of host publication	14th Asia-Pacific Web Conference, APWeb 2012, Proceedings
Publisher	Springer Verlag
Pages	652-659
Volume	7235 LNCS
ISBN (Print)	9783642292521
DOIs	https://doi.org/10.1007/978-3-642-29253-8_61
Publication status	Published - 2012
Event	14th Asia Pacific Web Technology Conference, APWeb 2012 - Kunming, China Duration: 11 Apr 2012 → 13 Apr 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7235 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th Asia Pacific Web Technology Conference, APWeb 2012
Country/Territory	China
City	Kunming
Period	11/04/12 → 13/04/12

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Han, H., Cai, Y., Shao, Y., & Li, Q. (2012). Improving recommendation based on features' co-occurrence effects in collaborative tagging systems. In Web Technologies and Applications: 14th Asia-Pacific Web Conference, APWeb 2012, Proceedings (Vol. 7235 LNCS, pp. 652-659). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7235 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-29253-8_61

Han, Hao ; Cai, Yi ; Shao, Yifeng et al. / Improving recommendation based on features' co-occurrence effects in collaborative tagging systems. Web Technologies and Applications: 14th Asia-Pacific Web Conference, APWeb 2012, Proceedings. Vol. 7235 LNCS Springer Verlag, 2012. pp. 652-659 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Improving recommendation based on features' co-occurrence effects in collaborative tagging systems",

abstract = "Currently, recommender system becomes more and more important and challenging, as users demand higher recommendation quality. Collaborative tagging systems allow users to annotate resources with their own tags which can reflect users' attitude on these resources and some attributes of resources. Based on our observation, we notice that there is co-occurrence effect of features, which may cause the change of user's favor on resources. Current recommendation methods do not take it into consideration. In this paper, we propose an assistant and enhanced method to improve the performance of other methods by combining co-occurrence effect of features in collaborative tagging environment. {\textcopyright} 2012 Springer-Verlag Berlin Heidelberg.",

author = "Hao Han and Yi Cai and Yifeng Shao and Qing Li",

year = "2012",

doi = "10.1007/978-3-642-29253-8_61",

language = "English",

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volume = "7235 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "14th Asia Pacific Web Technology Conference, APWeb 2012 ; Conference date: 11-04-2012 Through 13-04-2012",

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Han, H, Cai, Y, Shao, Y & Li, Q 2012, Improving recommendation based on features' co-occurrence effects in collaborative tagging systems. in Web Technologies and Applications: 14th Asia-Pacific Web Conference, APWeb 2012, Proceedings. vol. 7235 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7235 LNCS, Springer Verlag, pp. 652-659, 14th Asia Pacific Web Technology Conference, APWeb 2012, Kunming, China, 11/04/12. https://doi.org/10.1007/978-3-642-29253-8_61

Improving recommendation based on features' co-occurrence effects in collaborative tagging systems. / Han, Hao; Cai, Yi; Shao, Yifeng et al.
Web Technologies and Applications: 14th Asia-Pacific Web Conference, APWeb 2012, Proceedings. Vol. 7235 LNCS Springer Verlag, 2012. p. 652-659 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7235 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AB - Currently, recommender system becomes more and more important and challenging, as users demand higher recommendation quality. Collaborative tagging systems allow users to annotate resources with their own tags which can reflect users' attitude on these resources and some attributes of resources. Based on our observation, we notice that there is co-occurrence effect of features, which may cause the change of user's favor on resources. Current recommendation methods do not take it into consideration. In this paper, we propose an assistant and enhanced method to improve the performance of other methods by combining co-occurrence effect of features in collaborative tagging environment. © 2012 Springer-Verlag Berlin Heidelberg.

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Han H, Cai Y, Shao Y, Li Q. Improving recommendation based on features' co-occurrence effects in collaborative tagging systems. In Web Technologies and Applications: 14th Asia-Pacific Web Conference, APWeb 2012, Proceedings. Vol. 7235 LNCS. Springer Verlag. 2012. p. 652-659. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-29253-8_61

Improving recommendation based on features' co-occurrence effects in collaborative tagging systems

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