Abstract
It is difficult for forum administrators to grasp the timeliness and accuracy of forum hotspot distribution and changes, which is detrimental to the management of forum hotspot trends, and the timeliness and accuracy of user groups with the same concerns, which is not conducive to the forum user’s mutual communication. Therefore, a dynamic interest discovery model based on space-time vector is proposed. In this paper, based on the traditional vector space model (VSM), the TF-IDF algorithm is used to calculate the keyword weights of user interests and construct the user interest vector matrix. The dynamic discovery of the user’s focus through two dimensions of time and space, improving the timeliness and accuracy of the administrator’s grasp of the forum hotspots, and quickly and accurately finding the user groups with the same focus, is conducive to the realization of knowledge sharing. The case study shows that the dynamic discovery model of user interest based on space-time vectors can effectively find out the dynamic changes of the attention points of users and users that meet the concerns.
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References
Ren, B., Liang, Y., Zhao, J., Lian, W., Li, Y.: A user interest model based on multi-dimensional weights dynamic update. Comput. Eng. 40(9), 42–45 (2014)
Hao, S.: User interest model representation and update research for personalized services. Hefei University of Technology master’s degree thesis, Anhui (2012)
Song, Y., Chen, Z.: Research on user interest model in personalized retrieval system. Comput. Math. Eng. 41(2), 271–274 (2013)
Zhu, Q., Shyu, M., Wang, H.: Video topic: modeling user interests for content-based video recommendation. Int. J. Multimed. Data Eng. Manag. 5, 1–21 (2014)
Xian, X., Li, Y.: Proposed a Related PageRank algorithm based on the user’s interest. Adv. Mater. Res. 718-720, 2040–2044 (2013)
Qiu, Y., Wang, L., Shao, L.: User interest modeling method based on weibo short text. Comput. Eng. 40(2), 275–279 (2014)
Liang, X., Gu, L.: Chinese analysis and part of speech tagging. Comput. Technol. Dev. 25(2), 175–181 (2015)
Lin, H., Yang, Y.: User interest model representation and update mechanism. Comput. Res. Dev. 39(7), 843–847 (2002)
Teng, X.: Based on Struts2 and Hibernate community website system design and implementation. Huazhong University of Science and Technology master’s degree thesis, Wuhan (2011)
Diao, Z.: Research on personalized recommendation system based on ontology user interest model. Master’s degree thesis of Taiyuan University of Technology, Taiyuan (2013)
Chen, W., Zhang, X., Li, Z.: Analysis of constructing topic model of Weibo user interest model. Comput. Sci. 40(4), 127–130 (2013)
Hao, S., Wu, G., Hu, X.: User interest expression and updating based on hierarchical vector space model. J. Nanjing Univ. (Natural Science) 48(2), 190–197 (2012)
Tian, J.: Research on User Interest Model Based on Social Network. Master’s Degree in Electronic and Science University, Chengdu (2010)
Tao, W.: Realization of Chinese word segmentation algorithm based on bidirectional maximum matching method in policing applications. Electron. Technol. Softw., 153–155 (2016)
Liu, B.: Research on personalized user interest modeling in mobile environment. Master’s thesis, Beijing University of Posts and Telecommunications, Beijing (2009)
Qiu, J.: Research on user interest model based on Weibo social network. Shanghai Jiaotong University master’s degree thesis, Shanghai (2013)
Li, Z., Wang, D., Guan, Z.: Research on construction of user interest model based on domain ontology. J. Inf. Sci. 33(11), 69–73 (2015)
Ren, Y.: An improved method of judging the theme relativity based on vector space model in vertical search engine. Appl. Mech. Mater. 411-414, 106–109 (2013)
Zhao, Q., Niu, J., Chen, H.: An indicative opinion generation model for short texts on social networks. Future Gener. Comput. Syst. 86, 1471–1480 (2017)
Dong, Y., Liu, Y., Luo, J.: The optimization algorithm based on vector space model to determine the influence of microblogging text. Appl. Mech. Mater. 571-572, 278–281 (2014)
Cheng, S., Liu, Y.: Time-aware and grey incidence theory based user interest modeling for document recommendation. Cybern. Inf. Technol. 15, 36–52 (2015)
Acknowledgement
The thesis is supported by the National Key Research & Development Program of China under Grant NO. 2017YFB0803001, National Natural Science Foundation of China under Grant NO. 61370215, 61370211; and National Information Security Program of China under Grant NO. 2017A065, 2017A111.
First and foremost, I would like to show my deepest gratitude to my two teachers Prof. Chi and Prof. Zhang, who had provided me with valuable guidance in every stage of the writing of this thesis. Without their enlightening instruction, impressive kindness and patience, I could not have completed my thesis.
Then, I’d like to thank all my friends for their encouragement and support.
Last but not least, I shall extend my thanks the experts for taking the time to review my thesis and making valuable suggestions.
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Lin, J., Zhang, Z., Chi, L., Wang, Y. (2018). Research on Dynamic Discovery Model of User Interest Based on Time and Space Vector. In: Zhou, Q., Miao, Q., Wang, H., Xie, W., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 902. Springer, Singapore. https://doi.org/10.1007/978-981-13-2206-8_7
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DOI: https://doi.org/10.1007/978-981-13-2206-8_7
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