Abstract
Search engine marketing promoted by search engine companies, e,g., Google and Baidu, and the acknowledgment of brand promotion supported by the search engine have breaking through the limitation of traditional marketing model. However, with the ever-increasing complexity of internet ecosystem, how to improve the recommendation efficiency of e-commerce search advertisements has been conducting a joint academic/industry challenge. To address this issue, through analyzing the popular treatment schemes of search advertising, a recommendation scheme for e-commerce search advertisements using Spark based big data framework is proposed in this paper, which presents a solid solution to achieve high relevant recommendation for network users’ searching behaviors and information needs while implementing the tripartite benefit of network users, advertising platforms and advertisers. The conducted experiments have been shown to demonstrate the performance.
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Acknowledgments
This work was supported in part by the Natural Science Foundation of Guangdong Province, China (Grant No. 2018A030313014); Guangdong University Scientific Innovation Project (Grant No. 2017KTSCX178).
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Tao, M., Huang, P., Li, X., Ding, K. (2019). Big Data Based E-commerce Search Advertising Recommendation. In: Vaidya, J., Zhang, X., Li, J. (eds) Cyberspace Safety and Security. CSS 2019. Lecture Notes in Computer Science(), vol 11982. Springer, Cham. https://doi.org/10.1007/978-3-030-37337-5_37
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DOI: https://doi.org/10.1007/978-3-030-37337-5_37
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