Abstract
The traditional collaborative filtering algorithm ignores the impact of the time factor when searching the nearest neighbor set, only from the user or item takes into account the similarity of the user or item unilaterally, and ignores the impact of user characteristics on the recommendation. Aiming to the above problems, the paper introduced the time forgotten function, resources viscosity function and the user feature vector, also improved the process of finding the user’s nearest neighbor set, which reflected the time effect, degree of user preferences and user characteristic. The traditional algorithm consumes too many resources to search the nearest neighbor set, as well as the reliability is poor. In the paper, a novel recommendation algorithm based on user clustering of item attributes is proposed.
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References
Xia, Weiwei, Liang He, Lei Ren, et al. 2008. Improving collaborative filtering algorithms. In IEEE International Conference, December 2008, 1480–1484.
Su, Xiaoyuan, T.M. Khoshgoftaar, and R. Greiner. 2008. A collaborative filtering algorithm based on variance analysis of attributes-value preference. In IEEE/WIC/ACM International Conference, December 2008, vol. 1, 633–639.
Kim, B.M., Q. Li, C.S. Park, et al. 2006. A new approach for combining content—based and collaborative filters. Journal of Intelligent Information System 27 (I): 79–91.
Lee, J.S., C.H. Jun, J. Lee, et al. 2005. Classification based collaborative filtering using market basket data. Expert System with Applications 29 (3): 700–704.
Gong, Songjie, and Guanghua Cheng. 2008. Mining user interest change for improving collaborative filtering. In Intelligent Information Technology Application 2008. Second International Symposium 2008, vol. 3, 24–27.
Zheng, H., P. Li, and J. Wang. 2013. Context-aware scheduling algorithm in smart home system. China Communications 10 (11): 155–164.
Velasquez Juan, D., and Vasile Palade. 2007. Building a knowledge base for implementing a web based computerized recommendation system. International Journal on Artificial Intelligence Tools 16 (5): 793–828.
Zheng, L.T., G.J. Liu, C.G. Yan, and C.J. Jiang. 2018. Transaction fraud detection based on total order relation and behavior diversity. IEEE Transactions on Computational Social Systems 5 (3): 796–806.
Li, W., Y. Xia, M. Zhou, X. Sun, and Q. Zhu. Fluctuation-aware and predictive workflow scheduling in cost-effective infrastructure-as-a-service clouds. IEEE ACCESS, to be published. https://doi.org/10.1109/access.2018.2869827.
Ekstrand, M.D., M. Ludwig, J.A. Konstan, and J.T. Riedl. 2011. Rethinking the recommender re-search ecosystem: Reproducibility, openness, and lenskit. In Proceedings of the fifth ACM Conference on Recommender Systems 2011, 133–140.
Acknowledgements
We are pleased to acknowledge Reform and Construction of Undergraduate Experimental Practice Teaching in East China University of Science and Technology; the National Natural Science Foundation of China under Grant 61103115; the special fund for Software and Integrated Circuit Industry Development of Shanghai under Grant 150809; the “Action Plan for Innovation on Science and Technology” Projects of Shanghai (project No: 16511101000). The authors are also grateful to the anonymous referees for their insightful and valuable comments and suggestions.
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Luo, Y., Zheng, H. (2020). An Improved Algorithm of Mixed Cooperative Filter Recommendation Based on Project and User. In: Yang, CT., Pei, Y., Chang, JW. (eds) Innovative Computing. Lecture Notes in Electrical Engineering, vol 675. Springer, Singapore. https://doi.org/10.1007/978-981-15-5959-4_100
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DOI: https://doi.org/10.1007/978-981-15-5959-4_100
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