Abstract
The recommendation system is one of the most widely used applications in E-commerce. By studying the user’s preferences, we can recommend underlying contents for the user from the mass merchandise information. However, most recommendation systems pay much attention on popular products, just ignore those products that are currently not popular but potential for excavation. Our recommendation system based on RFN (Reverse Furthest Neighbor) queries follows the idea of mining popular products in the niche market. We improve the traditional collaborative filtering recommendation algorithm and adopt a collaborative filtering algorithm based on expert users. The modified algorithm can recommend products with potential value based on the power law, which make the distribution of minority mined more adequately by the users. The experimental results show that the recommendation system has high recommendation quality and practical value.
This work is supported by the National Natural Science Foundation of China (61672284, 41301407), the Funding of Security Ability Construction of Civil Aviation Administration of China (AS-SA2015/21), the Innovation Funding of Nanjing University of Aeronautics and Astronautics (NJ20160028, NT2018028, NS2018057).
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Wang, K., Li, B., Wan, S., Zhang, A., Guan, D. (2018). Research on Commodity Recommendation Algorithm Based on RFN. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_43
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