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Hybrid content filtering and reputation-based popularity for recommending blog articles

Duen-Ren Liu (Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan)
Chuen-He Liou (Center of General Education, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan)
Chi-Chieh Peng (Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan)
Huai-Chun Chi (Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan)

Online Information Review

ISSN: 1468-4527

Article publication date: 9 September 2014

Abstract

Purpose

Social bookmarking is a system which allows users to share, organise, search and manage bookmarks of web resources. However, with the rapid growth in the production of online documents, people are facing the problem of information overload. Social bookmarking web sites offer a solution to this by providing push counts, which are counts of users’ recommendations of articles, and thus indicate the popularity and interest thereof. In this way, users can use the push counts to find popular and interesting articles. A measure of popularity-based solely on push counts, however, cannot be considered a true reflection of popularity. The paper aims to discuss these issues.

Design/methodology/approach

In this paper, the authors propose to derive the degree of popularity of an article by considering the reputation of the users who push the article. Moreover, the authors propose a novel personalised blog article recommendation approach which combines reputation-based group popularity with content-based filtering (CBF), for the recommendation of popular blog articles which satisfy users’ personal preferences.

Findings

The experimental results show that the proposed approach outperforms conventional CBF, item-based and user-based collaborative filtering approaches. The proposed approach considering reputation-based group popularity scores on neighbouring articles indeed can improve the recommendation quality of traditional CBF method.

Originality/value

The recommendation approach modifies CBF method by considering the target user's group preferences, to overcome the limitation of CBF which arises from the recommending only items similar to those the user has previously liked. Users with similar article preferences (profiles) may form a group of users with similar interests. A group's preferences may also reflect an individual's preferences. The reputation-based group preferences of the target user's group can be used to complement the target user's preferences.

Keywords

Acknowledgements

This research was conducted in collaboration with funP company, and was supported in part by the National Science Council of Taiwan under Grants NSC 101-2410-H-009-007 and NSC 102-2410-H-227-009.

Citation

Liu, D.-R., Liou, C.-H., Peng, C.-C. and Chi, H.-C. (2014), "Hybrid content filtering and reputation-based popularity for recommending blog articles", Online Information Review, Vol. 38 No. 6, pp. 788-805. https://doi.org/10.1108/OIR-12-2013-0273

Publisher

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Emerald Group Publishing Limited

Copyright © 2014, Emerald Group Publishing Limited