Title: Hotspot prediction of e-commerce network users based on improved K-nearest neighbour algorithm
Authors: Gang Qiao
Addresses: Department of Economics and Management, Anhui Vocational College of Electronics and Information Technology, Bengbu 233060, China
Abstract: Aiming at the problems of low accuracy and poor prediction effect in traditional e-commerce network users' hot spot prediction, this paper proposes an e-commerce network users' hot spot prediction method based on improved K-nearest neighbour algorithm. According to the users' hotspot prediction impact indicator, the K-nearest neighbour algorithm is improved in pattern matching process. The key point method is used to remove the noise interference of the original time series. The dynamic time warping algorithm is used to measure the similarity of time series of users' hotspot. The distance weight and trend coefficient are introduced according to the difference of users' hotspot time series to deduce the future users' hotspot and realise hotspot prediction of e-commerce network users. Experimental results show that the method in this paper greatly reduces the deviation of prediction results, which fully shows that the method has better prediction effect.
Keywords: improved K-nearest neighbour algorithm; E-commerce network; hotspot prediction; distance weight; trend coefficient; simulation.
International Journal of Autonomous and Adaptive Communications Systems, 2022 Vol.15 No.3, pp.202 - 219
Received: 17 Jan 2020
Accepted: 18 May 2020
Published online: 09 Sep 2022 *