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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References
Parkes, D.C., Wellman, M.P.: Economic reasoning and artificial intelligence. Science 349(6245), 267–272 (2015)
Yang, Z., Shi, Y., Wang, B.: Search engine marketing, financing ability and firm performance in E-commerce. Procedia Comput. Sci. 55, 1106–1112 (2015)
Tao, M., Wei, W.H., Huang, S.Q.: Location-based trustworthy services recommendation in cooperative-communication-enabled internet of vehicles. J. Netw. Comput. Appl. 126, 1–11 (2019)
Rahman, M.M., Abdullah, N.A.: A personalized group-based recommendation approach for Web search in E-learning. IEEE Access 6, 34166–34178 (2018)
Hwang, W.H., Chen, Y.S., Jiang, T.M.: Personalized internet advertisement recommendation service based on keyword similarity. In: IEEE 39th Annual Computer Software and Applications Conference, vol. 1, pp. 29–33. IEEE, Taichung (2015). https://doi.org/10.1109/COMPSAC.2015.202
Siriaraya, P., Yamaguchi, Y., Morishita, M., et al.: Using categorized web browsing history to estimate the user’s latent interests for web advertisement recommendation. In: IEEE International Conference on Big Data (Big Data), pp. 4429–4434. IEEE, Boston (2017). https://doi.org/10.1109/BigData.2017.8258480
Gimenes, G., Cordeiro, R.L.F., Rodrigues-Jr, J.F.: ORFEL: efficient detection of defamation or illegitimate promotion in online recommendation. Inf. Sci. 379, 274–287 (2017)
Youssef, Y., Aly, S.G.: Towards the integration of diverse context into advertisement recommendation on mobile devices. In: IEEE 13th International Wireless Communications and Mobile Computing Conference (IWCMC), 1734–1739. IEEE, Valencia, Spain (2017). https://doi.org/10.1109/IWCMC.2017.7986546
Zhao, F., Zhou, J., Nie, C., et al.: Smartcrawler: a two-stage crawler for efficiently harvesting deep-web interfaces. IEEE Trans. Serv. Comput. 9(4), 608–620 (2016)
Dean, J., Ghemawat, S.: Mapreduce: a flexible data processing tool. Commun. ACM 53(1), 72–77 (2010)
Cheng, W., Ren, F., Jiang, W., et al.: Modeling and Analyzing Latency in the Memcached system. In: IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 538–548. IEEE, Atlanta (2017). https://doi.org/10.1109/ICDCS.2017.122
Siegal, D., Guo, J., Agrawal, G.: Smart-MLlib: a high-performance machine-learning library. In: IEEE International Conference on Cluster Computing (CLUSTER), pp. 336–345. IEEE, Taipei (2016). https://doi.org/10.1109/CLUSTER.2016.49
Liu, J., Pasupat, P., Wamg, Y., et al.: Query understanding enhanced by hierarchical parsing structures. In: IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 72–77. IEEE, Olomouc (2013). https://doi.org/10.1109/ASRU.2013.6707708
Jiang, M., Liu, R., Wang, F.: Word network topic model based on word2vector. In: IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService), pp. 241–247. IEEE, Bamberg (2018). https://doi.org/10.1109/BigDataService.2018.00043
Gao, H., Kong, D., Lu, M., et al.: Attention convolutional neural network for advertiser-level click-through rate forecasting. In: ACM 2018 World Wide Web Conference (WWW), pp. 1855–1864. ACM, Lyon (2018). https://doi.org/10.1145/3178876.3186184
Edizel, B., Mantrach, A., Bai, X., et al.: Deep character-level click-through rate prediction for sponsored search. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 305–314. ACM, Shinjuku (2017). https://doi.org/10.1145/3077136.3080811
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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