Abstract
Personalized services for information overload are becoming more common with the arrival of the era of big data. Massive information also makes the Internet platform pay more attention to the accuracy and efficiency of personalized recommendations. The user’s profile is constructed to describe the user information of the relevant platform more accurately and build virtual user features online through user behavior preference information accumulated on the platform. In this paper we propose a new user mode named user2vec for personalized recommendation. The construction of user2vec relies on platform and extremely targeted. At the same time, user profile is dynamically changing and need to be constantly updated according to the data and date, therefore we define a new time decay function to track time changes. Dynamic description of user behavior and preference information through user vectorization combined with time decay function can provide reference information for the platform more effectively. Finally, we using a layered structure to build an overall user profile system. And the experiment adapts content-based recommendation algorithm to indirectly prove effectiveness of user profile model. After many sets of experiments proved, it can be found that the proposed algorithm is effective and has certain guiding significance.
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
References
Mulder, S., Yarr, Z.: Win in the User. Machinery Industry Press, Beijing (2007)
Chen, S.T., Yu, T.J., Chen, L.C., et al.: A novel user profile learning approach with fuzzy constraint for news retrieval. Int. J. Intell. Syst. 32, 249–265 (2017)
Hawalah, A., Fasli, M.: Dynamic user profiles for web personalisation. Expert Syst. Appl. 42, 2547–2569 (2015)
De Amo, S., Diallo, M.S., et al.: Contextual preference mining for user profile construction. Inf. Syst. 49, 182–199 (2015)
Al-Shamri, M.Y.H.: User profiling approaches for demographic recommender systems. Knowl. Based Syst. 100, 175–187 (2016)
Peng, J., Choo, K.K.R., et al.: User profiling in intrusion detection: a review. J. Netw. Comput. Appl. 72, 14–27 (2016)
Liang, C.: User profile for personalized web search. In: International Conference on Fuzzy Systems and Knowledge Discovery, Shanghai, IEEE, pp. 1847–1850 (2011)
Kassak, O., Kompan, M., Bielikova, M.: User preference modeling by global and individual weights for personalized recommendation. Acta Polytech. Hung. 12(8), 27–41 (2015)
Chen, Y., Yu, Y., Zhang, W., et al.: Analyzing user behavior history for constructing user profile. In: Proceeding of 2008 IEEE International Symposium on IT in Medicine and Education, pp. 343–348. IEEE (2008)
Xie, H., Li, Q., Mao, X., et al.: Community-aware user profile enrichment in folksonomy. Neural Netw. 58, 111–121 (2014)
Cai, Y., Li, Q., Xie, H., et al.: Exploring personalized searches using tag-based user profiles and resource profiles in folksonomy. Neural Netw. 58, 98–110 (2014)
Tchuente, D., Canut, M., Jessel, N.P.: Visualizing the relevance of social ties in user profile modeling. Web Intell. Agent Syst. Int. J. 10, 261–274 (2012)
Du, Q., Xie, H., Cai, Y., et al.: Folksonomy-based personalized search by hybrid user profiles in multiple levels. Neurocomputing 204, 142–152 (2016)
Wu, Z., Zeng, Q., Hu, X.: Mining personalized user profile based on interesting points and interesting vectors. Inf. Technol. J. 8(6), 830–838 (2009)
Xie, H., Li, X., Wang, T., et al.: Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy. Inf. Process. Manag. 52, 61–72 (2016)
Amoretti, M., Belli, L., Zanichelli, F.: UTravel: smart mobility with a novel user profiling and recommendation approach. Pervasive Mob. Comput. 38, 474–489 (2017)
Sahijwani, H., Dasgupta, S.: User Profile Based Research Paper Recommendation. https://uspmes.daiict.ac.in/btpsite. Accessed 5 June 2018
Ouaftouh, S., Zellou, A., Idri, A.: UPCAR: user profile clustering based approach for recommendation. In: International Conference on Education Technology and Computers, Association for Computing Machinery, Barcelona, pp. 17–21 (2017)
Mikolov, T., Chen, K., Corrado, G., et al.: Efficient estimation of word representations in vector space, pp. 1–12 (2013)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning, Beijing, China, JMLR W&CP, vol. 32 (2014)
Ranking Algorithm Based on User Voting (4): Newton’s Cooling La. https://blog.csdn.net/zhuhengv/article/details/50476306. Accessed 5 June 2018
Aggarwal, C.C.: Recommender Systems: The Textbook. Springer, Basel (2016)
Gasparic, M.: Context-based IDE command recommender system. In: RecSys 2016, September 15–19, Boston, MA, USA, pp. 435–438 (2016)
Acknowledgments
We would like to thank all colleagues and students who helped for our work. This research was partially supported by (1) National Social Science Fund Project: Research on Principles and Methods of Electronic Document Credential Guarantee in Cloud Computing Environment (Project No. 15BTQ079); (2) Special Project for Civil Aircraft, MIIT; (3) Fund of Shanghai Engineering Research Center of Civil Aircraft Health Monitoring.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Y., Jin, F., Su, H., Wang, J., Zhang, G. (2018). Reasearch on User Profile Based on User2vec. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-02934-0_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02933-3
Online ISBN: 978-3-030-02934-0
eBook Packages: Computer ScienceComputer Science (R0)