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A Model for Computing User’s Preference Based on EP Algorithm

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 670))

Abstract

In this paper, we address the problem of identifying target user through the model of computing user preference for a certain item or service. The model we present works for a specific domain through online behavior analysis which considers user’s attentiveness of the entire area and the specific item combination style combining features of the specific industry. The model is evaluated by predicting users’ behavior and advertising click-through rate in the real application environment. The results show that this model is successful in precision recommendation, especially for the dynamic data analysis.

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References

  1. Chen Y C, Hsieh H C, Lin H C. Improved precision recommendation scheme by BPNN algorithm in O2O commerce [C]//e-Business Engineering (ICEBE), 2013 IEEE 10th International Conference on. IEEE, 2013: 324–328.

    Google Scholar 

  2. Chung N S, Kim C H, Oh T S, et al. Development of a Knowledge-Based Crop Recommendation Model for Precision Agriculture [J]. The Journal of the Korean Society of International Agriculture, 2013.

    Google Scholar 

  3. Zhai X, Jin F, Wang J, et al. A Kind of Precision Recommendation Method for Massive Public Digital Cultural Resources: A Preliminary Report [C]//Multimedia Big Data (BigMM), 2016 IEEE Second International Conference on. IEEE, 2016: 56–59.

    Google Scholar 

  4. Hu J, Gao Z, Pan W. Multiangle Social Network Recommendation Algorithms and Similarity Network Evaluation [J]. Journal of Applied Mathematics, 2013, 2013.

    Google Scholar 

  5. Liu B, Fu Y, Yao Z, et al. Learning geographical preferences for point-of-interest recommendation [C]//Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2013: 1043–1051.

    Google Scholar 

  6. Gao H, Tang J, Hu X, et al. Exploring temporal effects for location recommendation on location-based social networks [C]//Proceedings of the 7th ACM conference on Recommender systems. ACM, 2013: 93–100.

    Google Scholar 

  7. Liu X, Liu Y, Aberer K, et al. Personalized point-of-interest recommendation by mining users’ preference transition [C]//Proceedings of the 22nd ACM international conference on Information & Knowledge Management. ACM, 2013: 733–738.

    Google Scholar 

  8. Gandhi M, Mistry K, Patel M. A Modified Approach towards Tourism Recommendation System with Collaborative Filtering and Association Rule Mining [J]. International Journal of Computer Applications, 2014, 91(6).

    Google Scholar 

  9. Liu R S, Yang T C. Improving Recommendation Accuracy by Considering Electronic Word-of-Mouth and the Effects of Its Propagation Using Collective Matrix Factorization [C]//Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), 2016 IEEE 14th Intl C. IEEE, 2016: 696–703.

    Google Scholar 

  10. Tao Y. Design of large scale mobile advertising recommendation system [C]//Computer Science and Network Technology (ICCSNT), 2015 4th International Conference on. IEEE, 2015, 1: 763–767.

    Google Scholar 

  11. Feng Y, Tang R, Zhai Y, et al. Personalized media recommendation algorithm based on smart home [C]//The Second International Conference on e-Technologies and Networks for Development. The Society of Digital Information and Wireless Communication, 2013: 63–67.

    Google Scholar 

  12. Li J, Xia F, Wang W, et al. Acrec: a co-authorship based random walk model for academic collaboration recommendation [C]//Proceedings of the 23rd International Conference on World Wide Web. ACM, 2014: 1209–1214.

    Google Scholar 

  13. Ju C, Xu C. A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm [J]. The Scientific World Journal, 2013, 2013.

    Google Scholar 

  14. Bashir M A, Arshad S, Wilson C. Recommended For You: A First Look at Content Recommendation Networks [C]//Proceedings of the 2016 ACM on Internet Measurement Conference. ACM, 2016: 17–24.

    Google Scholar 

  15. Venkatraman V, Dimoka A, Pavlou P A, et al. Predicting advertising success beyond traditional measures: New insights from neurophysiological methods and market response modeling [J]. Journal of Marketing Research, 2015, 52(4): 436–452.

    Google Scholar 

  16. Wang Y, Feng D, Li D, et al. A mobile recommendation system based on logistic regression and Gradient Boosting Decision Trees [C]//Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE, 2016: 1896–1902.

    Google Scholar 

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Acknowledgements

This work was partially supported by GDNSF fund (2015A030313782), SUSTech Starup fund (Y01236215), SUSTech fund (05/Y01051814, 05/Y01051827, 05/Y01051830, and 05/Y01051839).

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Correspondence to Zongwei Luo .

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Jiang, S., Luo, Z., Huang, Z., Liu, J. (2019). A Model for Computing User’s Preference Based on EP Algorithm. In: Panigrahi, B., Trivedi, M., Mishra, K., Tiwari, S., Singh, P. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 670. Springer, Singapore. https://doi.org/10.1007/978-981-10-8971-8_11

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