Abstract
Tagging is a popular way to categorize and search online content, and tag recommendation has been widely studied to better support automatic tagging. In this work, we focus on recommending tags for content-based applications such as blogs and question-answering sites. Our key observation is that many tags actually have appeared in the content in these applications. Based on this observation, we first model the tag recommendation problem as a multi-label learning problem and then further incorporate keyword extraction to improve recommendation accuracy. Moreover, we speedup the proposed method using a locality-sensitive hashing strategy. Experimental evaluations on two real data sets demonstrate the effectiveness and efficiency of our proposed methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: 47th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2006, pp. 459–468. IEEE (2006)
Belém, F., Martins, E., Pontes, T., Almeida, J., Gonçalves, M.: Associative tag recommendation exploiting multiple textual features. In: SIGIR, pp. 1033–1042. ACM (2011)
Feng, W., Wang, J.: Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1276–1284. ACM (2012)
Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in social bookmarking systems. AI Communication 21(4), 231–247 (2008)
Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 61–68. ACM (2009)
Lipczak, M., Milios, E.: Learning in efficient tag recommendation. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 167–174. ACM (2010)
Liu, R., Niu, Z.: A collaborative filtering recommendation algorithm based on tag clustering. In: Park, J.J.(J.H.), Stojmenovic, I., Choi, M., Xhafa, F. (eds.) Future Information Technology. LNEE, vol. 276, pp. 177–183. Springer, Heidelberg (2014)
Lu, Y.T., Yu, S.I., Chang, T.C., Hsu, J.Y.J.: A content-based method to enhance tag recommendation. In: IJCAI, vol. 9, pp. 2064–2069 (2009)
Mihalcea, R., Tarau, P.: Textrank: Bringing order into texts. Association for Computational Linguistics (2004)
Murfi, H., Obermayer, K.: A two-level learning hierarchy of concept based keyword extraction for tag recommendations. In: ECML PKDD Discovery Challenge (DC 2009), p. 201 (2009)
Rendle, S., Balby Marinho, L., Nanopoulos, A., Schmidt-Thieme, L.: Learning optimal ranking with tensor factorization for tag recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 727–736. ACM (2009)
Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 81–90. ACM (2010)
Saha, A.K., Saha, R.K., Schneider, K.A.: A discriminative model approach for suggesting tags automatically for stack overflow questions. In: Proceedings of the 10th Working Conference on Mining Software Repositories, pp. 73–76. IEEE Press (2013)
Si, X., Sun, M.: Tag-lda for scalable real-time tag recommendation. Journal of Computational Information Systems 6(1), 23–31 (2009)
Sigurbjörnsson, B., Van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: Proceedings of the 17th International Conference on World Wide Web, pp. 327–336. ACM (2008)
Song, Y., Zhang, L., Giles, C.L.: Automatic tag recommendation algorithms for social recommender systems. ACM Transactions on the Web (TWEB)Â 5(1), 4 (2011)
Subramaniyaswamy, V., Pandian, S.C.: Topic ontology-based efficient tag recommendation approach for blogs. International Journal of Computational Science and Engineering 9(3), 177–187 (2014)
Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 43–50. ACM (2008)
Wang, J., Hong, L., Davison, B.D.: Rsdc’09: Tag recommendation using keywords and association rules. In: ECML PKDD Discovery Challenge, pp. 261–274 (2009)
Wang, T., Wang, H., Yin, G., Ling, C.X., Li, X., Zou, P.: Tag recommendation for open source software. Frontiers of Computer Science 8(1), 69–82 (2014)
Xia, X., Lo, D., Wang, X., Zhou, B.: Tag recommendation in software information sites. In: Proceedings of the 10th Working Conference on Mining Software Repositories, pp. 287–296. IEEE Press (2013)
Zhang, M.L., Zhou, Z.H.: Ml-knn: A lazy learning approach to multi-label learning. Pattern Recognition 40(7), 2038–2048 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, L., Yao, Y., Xu, F., Lu, J. (2015). MATAR: Keywords Enhanced Multi-label Learning for Tag Recommendation. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-25255-1_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25254-4
Online ISBN: 978-3-319-25255-1
eBook Packages: Computer ScienceComputer Science (R0)