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Tag recommendation method in folksonomy based on user tagging status

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Abstract

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.

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Notes

  1. CiteULike Datasets, http://www.citeulike.org/faq/data.adp2012,10,17

  2. HetRec 2011 Data Sets, http://grouplens.org/datasets/hetrec-2011/2012,10,1

References

  • Batagelj, V., & Zaveršnik, M. (2011). Fast algorithms for determining (generalized) core groups in social networks. Advances in Data Analysis and Classification, 5(2), 129–145.

    Article  MathSciNet  MATH  Google Scholar 

  • Belém, F.M., Martins, E.F., Almeida, J.M., & Goncal, M.A. (2014). Personalized and object-centered tag recommendation methods for web 2.0 applications. Information Processing and Management, 50, 524C553.

    Article  Google Scholar 

  • 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.

  • 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.

  • Hu, J., Wang, B., Liu, Y., & Li, D. (2012). Personalized tag recommendation using social influence. Journal of Computer Science and Technolgy, 27(3), 527–540.

    Article  Google Scholar 

  • Jäschke, R, Marinho, L., Hotho, A., Schmidt-Thieme, L., & Stumme, G. (2008). Tag recommendations in social bookmarking systems. Ai Communications, 21 (4), 231–247.

    MathSciNet  MATH  Google Scholar 

  • Kim, H., & Kim, H.J. (2014). A framework for tag-aware recommender systems. Expert Systems with Applications, 41, 4000–4009.

    Article  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 

  • Krestel, R., & Fankhauser, P. (2012). Personalized topic-based tag recommendation. Neurocomputing, 76(1), 61–70.

    Article  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.

  • Liu, K., Fang, B., & Zhang, W. (2011). Exploring social relations for personalized tag recommendation in social tagging systems. IEICE Transactions on Information and Systems, 94-D(3), 542–551.

    Article  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.

  • 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 

  • Ma, T., Zhou, J., Tang, M., Tian, Y., Al-Dhelaan, A., Al-Rodhaan, M., & Lee, S. (2015). Social network and tag sources based augmenting collaborative recommender system. IEICE Transactions on Information and Systems, 98(4), 902–910.

    Article  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.

  • 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 

  • Ramezani, M. (2011). Improving graph-based approaches for personalized tag recommendation. Journal of Emerging Technologies in Web Intelligence, 3(2), 168–176.

    Article  MathSciNet  Google Scholar 

  • Rawashdeh, M., Kim, H.N., Alja’am, J.M., & Saddik, A.E. (2013). Folksonomy link prediction based on a tripartite graph for tag recommendation. Journal of Intelligent Information Systems, 40, 307–325.

    Article  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).

  • Vander Wal, T. (2007). Folksonomy. http://vanderwal.net/folksonomy.html.

  • Wang, H., & Yeung, D.Y. (2016). Towards bayesian deep learning: A framework and some existing methods. IEEE Transactions on Knowledge and Data Engineering, 28(12), 3395–3408.

    Article  Google Scholar 

  • 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.

  • Wei, S., Zheng, X., Chen, D., & Chen, C. (2016). A hybrid approach for movie recommendation via tags and ratings. Electronic Commerce Research and Applications, 18, 83–94.

    Article  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.

  • Xie, H., Li, X., Wang, T., Lau, R.Y., Wong, T.L., Chen, L., Wang, F.L., & Li, Q. (2016). Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy. Information Processing and Management, 52(1), 61–72.

    Article  Google Scholar 

  • Zhang, Y., Zhang, B., Gao, K., Guo, P., & Sun, D. (2012). Combining content and relation analysis for recommendation in social tagging systems. Physica A: Statistical Mechanics and its Applications, 391(22), 5759–5768.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under grant No.61379114 and No.61533020.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Hong Yu, Mingyao Deng & Feng Hu

  2. Department of Computer Science, Sam Houston State University, Huntsville, TX, 77341, USA

    Bing Zhou

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  1. Hong Yu
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  2. Bing Zhou
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  3. Mingyao Deng
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  4. Feng Hu
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Correspondence to Hong Yu.

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Yu, H., Zhou, B., Deng, M. et al. Tag recommendation method in folksonomy based on user tagging status. J Intell Inf Syst 50, 479–500 (2018). https://doi.org/10.1007/s10844-017-0468-1

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  • DOI: https://doi.org/10.1007/s10844-017-0468-1

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