Abstract
In order to improve users’ experience it is necessary to recommend valuable and interesting content for users. A tag probability correlation based microblog recommendation method (TPCMR) is presented via analyzing microblog features and the deficiencies of existing microblog recommendation algorithm. Firstly, our method takes advantage of the probability correlation between tags to construct the tag similarity matrix. Then the weight of the tag for each user is enhanced based on the relevance weighting scheme and the user tag matrix can be constructed. The matrix is updated using the tag similarity matrix, which contains both the user interest information and the relationship between tags and tags. Experimental results show that the algorithm is effective for microblog recommendation.
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References
Huang, X., Chen, H., Liu, Y., Xiong, L.: A novel algorithm for feature selectionon micro-blog short text. Comput. Eng. Sci. 37(9), 1761–1767 (2015)
Sun, A.: Short text classification using very few words. In: The 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2012), Portland, pp. 1145–1146 (2012)
Liu, W., Quan, X., Feng, M., Qiu, B.: A short text modeling method combining semantic and statistical information. Inf. Sci. 180(20), 4031–4041 (2010)
Xing, Q., Liu, L., Liu, Y., Zhang, M., Ma, S.: Study on user tags in Weibo. J. Softw. 26(7), 1626–1637 (2015)
Yamaguchi, Y., Amagasa, T., Kitagawa, H.: Tag-based user topic discovery using Twitter lists. In: International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011), Kaohsiung, pp. 13–20 (2011)
Ma, H., Jia, M., Xie, M., Lin, X.: A microblog recommendation algorithm based on multi-tag correlation. In: Zhang, S., Wirsing, M., Zhang, Z. (eds.) KSEM 2015. LNCS, vol. 9403, pp. 483–488. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25159-2_43
Song, S., Zhu, H., Chen, L.: Probabilistic correlation-based similarity measure on text records. Inf. Sci. 289(1), 8–24 (2014)
Zhou, X., Wu, S., Chen, C., Chen, G., Ying, S.: Real-time recommendation for microblogs. Inf. Sci. 279(279), 301–325 (2014)
Phan, X., Nguyen, L., Horiguchi, S.: Learning to classify short and sparse text and web with hidden topics from large-scale data collections. In: The 17th International Conference on World Wide Web (WWW2008), Beijing, pp. 91–100 (2008)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61363058, 61165002), Youth Science and technology support program of Gansu Province(145RJZA232, 145RJYA259).
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Zhang, D., Ma, H., Jia, J., Yu, L. (2016). A Tag Probability Correlation Based Microblog Recommendation Method. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_55
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DOI: https://doi.org/10.1007/978-3-319-46672-9_55
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