Tag recommendation method in folksonomy based on user tagging status
A folksonomy consists of three basic entities, namely users, tags and resources. This kind of social tagging system is a good way to index information, facilitate searches and navigate resources. The main objective of this paper is to present a novel method to improve the quality of tag recommendation. According to the statistical analysis, we find that the total number of tags used by a user changes over time in a social tagging system. Thus, this paper introduces the concept of user tagging status, namely the growing status, the mature status and the dormant status. Then, the determining user tagging status algorithm is presented considering a user’s current tagging status to be one of the three tagging status at one point. Finally, three corresponding strategies are developed to compute the tag probability distribution based on the statistical language model in order to recommend tags most likely to be used by users. Experimental results show that the proposed method is better than the compared methods at the accuracy of tag recommendation.
KeywordsSocial tagging Tag recommendation Tagging status Probability distribution Folksonomy
This work was supported in part by the National Natural Science Foundation of China under grant No.61379114 and No.61533020.
- Cai, X., Zhu, J., Shen, B., & Chen, Y. (2016). Greta: Graph-based tag assignment for github repositories. In IEEE 40th annual computer software and applications conference (COMPSAC), 2016 (Vol. 1, pp. 63–72). IEEE.Google Scholar
- Feng, W., & Wang, J. (2012). Incorporating heterogeneous information for personalized tag recommendation in social tagging systems, Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1276–1284). New York, USA: ACM.Google Scholar
- Gemmell, J., Schimoler, T., Mobasher, B., & Burke, R. (2010). Hybrid tag recommendation for social annotation systems, Proceedings of the 19th ACM international conference on information and knowledge management (pp. 829–838). New York, USA: ACM.Google Scholar
- Hmimida, M., & Kanawati, R. (2016). A graph-based meta-approach for tag recommendation. In International workshop on complex networks and their applications (pp. 309–320). Springer.Google Scholar
- Kim, H.N., & El Saddik, A. (2011). Personalized pagerank vectors for tag recommendations: inside folkrank, Proceedings of the fifth ACM conference on recommender systems. RecSys ’11, pp 45–52. New York, USA: ACM.Google Scholar
- Kubatz, M., Gedikli, F., & Jannach, D. (2011). Localrank - neighborhood-based, fast computation of tag recommendations. In EC-Web, lecture notes in business information processing, (Vol. 85, pp. 258–269). Springer.Google Scholar
- Liu, Z., Shi, C., & Sun, M. (2010). Folkdiffusion: A graph-based tag suggestion method for folksonomies. In Cheng, P.J., Kan, M.Y., Lam, W., & Nakov, P. (Eds.) AAIRS, lecture notes in computer science (Vol. 6458, pp. 231240). Springer.Google Scholar
- Lu, C., Hu, X., Park, Jr, & Jia, H. (2011). Post-based collaborative filtering for personalized tag recommendation, Proceedings of the 2011 iConference (pp. 561–568). New York, USA: ACM.Google Scholar
- Marinho, L.B., Nanopoulos, A., Schmidt-Thieme, L., Jäschke, R., Hotho, A., Stumme, G., & Symeonidis, P. (2011). Social tagging recommender systems. In Recommender systems handbook (pp. 615–644). Springer.Google Scholar
- Ponte, J.M., & Croft, W.B. (1998). A language modeling approach to information retrieval, Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval (pp. 275–281). New York, USA: ACM.Google Scholar
- Sood, S.C., Owsley, S.H., Hammond, K.J., & Birnbaum, L. (2007). Tagassist: Automatic tag suggestion for blog posts, ICWSM.Google Scholar
- Trant, J. (2009). Studying social tagging and folksonomy: A review and framework. Journal of Digital Information, 10(1).Google Scholar
- Vander Wal, T. (2007). Folksonomy. http://vanderwal.net/folksonomy.html.
- Wang, H., Chen, B., & Li, W.J. (2013). Collaborative topic regression with social regularization for tag recommendation, IJCAI.Google Scholar
- Wang, H., Wang, N., & Yeung, D.Y. (2015). Collaborative deep learning for recommender systems, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1235–1244): ACM.Google Scholar
- Wu, Y., Yao, Y., Xu, F., Tong, H., & Lu, J. (2016). Tag2word: Using tags to generate words for content based tag recommendation. In Proceedings of the 25th ACM international on conference on information and knowledge management (pp. 22872292). ACM.Google Scholar