|
|
|
| Tag Recommendation Method Considering Users Tagging Status |
| YU Hong, DENG Ming-Yao, HU Feng |
| Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommunications, Chongqing 400065 |
|
|
|
|
Abstract To improve the quality of tag recommendation, a tag recommendation method considering users current tagging status is proposed. Firstly, the statistical analysis shows the total number of tags used by a user is changed with time in a social tagging system. Then, three tagging statuses are defined, i.e. the growing status, the mature status and the dormant status, and a user current tagging status is one of the above statuses. Finally, according to the characteristics of the current tagging status, different strategies are developed to compute the tag probability distribution to recommend tags to users. Results of comparative experiments show that the proposed method has better accuracy of tag recommendation.
|
|
Received: 26 June 2013
|
|
|
|
|
|
[1] Hamouda S, Wanas N M. PUT-Tag: Personalized User-Centric Tag Recommendation for Social Bookmarking Systems. Social Network Analysis and Mining, 2011, 1(4): 377-385
[2] Lu C M, Hu X H, Park J R, et al. Post-Based Collaborative Fil-tering for Personalized Tag Recommendation // Proc of the iConfe-rence. Seattle, USA, 2011: 561-568
[3] Liu K P, Fang B X, Zhang W Z. Exploring Social Relations for Personalized Tag Recommendation in Social Tagging Systems. IEICE Trans on Information and Systems, 2011, 94-D(3): 542-551
[4] Jschke R, Marinho L, Hotho A, et al. Tag Recommendations in Social Bookmarking Systems. AI Communications, 2008, 21(4): 231-247
[5] Kim H N, El Saddik A. Personalized PageRank Vectors for Tag Recommendations: Inside FolkRank // Proc of the 5th ACM Conference on Recommender Systems. Chicago, USA, 2011: 45-52
[6] Kubatz M, Gedikli F, Jannach D. LocalRank-Neighborhood-Based, Fast Computation of Tag Recommendations // Proc of the 12th International Conference on E-commerce and Web Technologies. Toulouse, France, 2011: 258-269
[7] Ramezani M. Improving Graph-Based Approaches for Personalized Tag Recommendation. Journal of Emerging Technologies in Web Intelligence, 2011, 3(2): 168-176
[8] Krestel R, Fankhauser P. Personalized Topic-Based Tag Recommendation. Neurocomputing, 2012, 76(1): 61-70
[9] Zhang B, Zhang Y, Gao K N, et al. Combining Relation and Content Analysis for Social Tagging Recommendation. Journal of Software, 2012, 23(3): 476-488 ( in Chinese)
(张 斌,张 引,高克宁,等.融合关系与内容分析的社会标签推荐.软件学报, 2012, 23(3): 476-488)
[10] Feng W, Wang J Y. Incorporating Heterogeneous Information for Personalized Tag Recommendation in Social Tagging Systems // Proc of the 18th ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining. Beijing, China, 2012: 1276-1284
[11] Nieminen I T. Combining Tag Recommendations Based on User History [EB/OL].[2013-04-20]. http:// ceur-ws.org/vol_497/paper_08.pdf
[12] Zhang Y, Zhang N, Tang J. A Collaborative Filtering Tag Recommendation System Based on Graph // Proc of the European Conference on Machine Learning and Principles and Practice of Know-ledge Discovery in Databases. Bled, Slovenia, 2009: 297-306
[13] Gemmell J, Schimoler T, Ramezani M, et al. Improving FolkRank with Item-Based Collaborative Filtering // Proc of the 3rd ACM Conference on Recommender System. New York, USA, 2009: 17-24
[14] Ponte J M, Croft W B. A Language Modeling Approach to Information Retrieval // Proc of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Melbourne, Australia, 1998: 275-281
[15] Jschke R, Marinho L, Hotho A, et al. Tag Recommendations in Folksonomies // Proc of the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases. Warsaw, Poland, 2007: 506-514
[16] Batagelj V, Zavernik M. Fast Algorithms for Determining (Gene-ralized) Core Groups in Social Networks. Advances in Data Analysis and Classification, 2011, 5(2): 129-145 |
|
|
|
2014, Vol. 27 
