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Research on Recommendation Mechanism and Algorithm of Digital Intelligent Media Technology

Published: 21 December 2023 Publication History

Abstract

With the intelligent and digital development of media technology, people are eager to obtain the information they need at a faster speed. Therefore, the recommendation system has become one of the most concerned research topics in the era of big data. Based on the in-depth analysis of existing research, this paper conducts research on the optimization of information recommendation algorithms that integrate clustering mixed information recommendation algorithms, which can make full and efficient use of additional auxiliary information, so that the model can collect, analyze, and integrate the characteristics of news information, adjust The user's interest preferences can greatly improve the performance of the recommendation system.

References

[1]
Guo Naicheng,Liu Xiaolei,Li Shaoshuai,et al.Poincare Heterogeneous Graph Neural Networks for Sequential Recommendation[J].ACM Transactions on Information Systems, 2022,41(3):1-26
[2]
Santoro A,Bartunov S,Botvinick M,et al.Meta-Learning with Memory-Augmented Neural Networks[C]//The 33rd International Conference on International Conference on Machine Learning.2016,48:1842-1850
[3]
Junyoung chung,caglar gulcehre,kyunghyun cho,and yoshua bengio.2014. empirical evaluation of gated recurrent neural networks on sequence modeling.arxiv preprint arxiv:1412.3555 (2014).
[4]
Zihang dai,zhilin yang,yiming yang,william cohen,jaime carbonell,quocv le,and ruslan salakhutdinov.2019.transformer-xl:Attentive language models beyond a fixed-length context.Arxiv preprint arxiv:1901.02860 (2019).
[5]
Qiang cui,shu wu,yan huang,and liang wang.2017.a hierarchical contextualattention-based gru network for sequential recommendation.arxiv preprintarxiv:1711.05114(2017).
[6]
Wang Hongwei,Zhang Fuzheng,Xie Xing,et al.DKN:Deep knowledge-aware network for news recommendation[C]//Proceedings of the 2018 world wide web conference.2018:1835-1844.

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          ICIIP '23: Proceedings of the 2023 8th International Conference on Intelligent Information Processing
          November 2023
          341 pages
          ISBN:9798400708091
          DOI:10.1145/3635175
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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 21 December 2023

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          Author Tags

          1. algorithm analysis
          2. digitization
          3. intelligence
          4. media technology
          5. recommendation system

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          ICIIP 2023

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          Overall Acceptance Rate 87 of 367 submissions, 24%

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