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A novel high-utility association rule mining method and its application to movie recommendation

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Abstract

The rapid development of the Internet and information technology has caused a surge in the amount of information in the network, and the problem of "information overload" has affected the user experience. The research object of this paper is the movie recommendation system, mainly from the algorithm of recommendation technology, and the recommendation effect is verified by simulation experiments on the Movielens dataset. High-utility association rule mining is applied to the recommendation algorithm, and the sparse scoring matrix is filled with high-utility itemsets. The experimental results show that the proposed high-efficiency mining method can effectively fill the user rating matrix, and its precision is increased by 9.59% compared with the traditional algorithm, the recall rate is increased by 8.97%, and the coverage rate is increased by 9.30%.It is proposed that the temporal distance is used to process the user rating time series, and then the temporal similarity is used to calculate the user interest. The experimental results show that the precision of the proposed method is improved by 1.32% and the recall rate is improved by 1.57% compared with the above algorithms. This method can also be applied to other recommender systems.

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Data availability

The datasets generated during and analysed during the current study are available in the repository, https://grouplens.org/datasets/movielens/100k/.

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Authors and Affiliations

  1. School of Science, Wuhan University of Technology, Wuhan, China

    Xiuhan Jiang & Xi Fang

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  1. Xiuhan Jiang
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  2. Xi Fang
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Correspondence to Xi Fang.

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Jiang, X., Fang, X. A novel high-utility association rule mining method and its application to movie recommendation. Multimed Tools Appl 83, 41033–41049 (2024). https://doi.org/10.1007/s11042-023-17063-5

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