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

Author

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  • Hongli Wan
  • Yuchen Li

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.

Suggested Citation

  • Hongli Wan & Yuchen Li, 2021. "A personalised recommendation algorithm of user preference products based on Bayesian network," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 25(2), pages 85-100.
    • Handle: RePEc:ids:ijpdev:v:25:y:2021:i:2:p:85-100
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