Title: A personalised recommendation algorithm of user preference products based on Bayesian network

Authors: Hongli Wan; Yuchen Li

Addresses: School of Information & Software, Dalian Neusoft University of Information, Dalian 116023, China ' School of Electrical Engineering, Dalian University of Science and Technology, Dalian 116052, China

Abstract: In order to overcome the problems of low recommendation accuracy, coverage rate and user diversity index in current personalised recommendation algorithms for user preference products, a new personalised recommendation algorithm based on Bayesian network is proposed. The algorithm takes into account the changing rule of users' interest characteristics with time, and divides the friendly neighbour network. The tags that users are interested in are obtained by user tag information and network partition results, the user's preference for products is obtained by combining with Bayesian network, and personalised products are recommended for users according to the results of preference calculation. The simulation results show that the proposed algorithm can effectively increase the accuracy, coverage and diversity index of user preference products, and recommend the most satisfactory products for users.

Keywords: Bayesian network; user preference; product; personalised recommendation.

DOI: 10.1504/IJPD.2021.116147

International Journal of Product Development, 2021 Vol.25 No.2, pp.85 - 100

Received: 26 Oct 2020
Accepted: 14 Mar 2021
Published online: 12 Jul 2021 *

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