Abstract
In recent years, with the rapidly development of Internet and pharmaceutical market, online medical platform has become a major place for online medical trading. Recommendation systems have been widely deployed in commercial platform to improve user experience and sales. Motivated by this, we propose two hybrid recommendation algorithms, CB-CF hybrid algorithm and CNN-based CF algorithm, for B2B medical platform to provide accurate recommendations. We also give a brief introduction of two well-known recommendation algorithms, content-based algorithm and model-based CF algorithm. Then we investigate the performance of recommendation algorithms on Apache Spark and Tensorflow with real-world data collected from a china B2B online medical platform. Experimental results show that the hybrid recommendation algorithm performs better than other algorithms.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61571141, Grant No. 61702120); Guangdong Natural Science Foundation (Grant No. 2014A030313130); The Excellent Young Teachers in Universities in Guangdong (Grant No. YQ2015105); Guangdong Provincial Application-oriented Technical Research and Development Special fund project (Grant No. 2015B010131017, No. 2017B010125003); Science and Technology Program of Guangzhou (Grant No. 201604016108); Guangdong Future Network Engineering Technology Research Center (Grant No. 2016GCZX006); Science and Technology Project of Nan Shan (2017CX004); The Project of Youth Innovation Talent of Universities in Guangdong (No. 2017KQNCX120); Guangdong science and technology development project (2017A090905023).
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Dai, Q. et al. (2018). Deep Learning Based Recommendation Algorithm in Online Medical Platform. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_4
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DOI: https://doi.org/10.1007/978-3-030-00563-4_4
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