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QCF: Quantum Collaborative Filtering Recommendation Algorithm

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Abstract

With the rapid development of the Internet, e-commerce plays an important role in people’s lives, and the recommendation system is one of the most critical technologies. However, as the number of users and the scale of goods increase sharply, the traditional collaborative filtering recommendation algorithm has a large computational complexity in the part of calculating the user similarity, which leads to a low recommendation efficiency. In response to the above problems, this paper introduces the concept of quantum computing theory. The user score vector is first prepared into a quantum state, the similarity score is calculated in parallel, then the similarity information is saved into the quantum bit, and finally the similar user is searched by the Grover search algorithm. Compared with the traditional collaborative filtering recommendation algorithm, the time complexity of the collaborative filtering recommendation algorithm based on Grover algorithm can be effectively reduced under certain conditions.

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Acknowledgements

This work is supported by National Natural Science Foundation of China (61802033, 61751110), supported by Postdoctoral research Foundation of China (216638), and also supported by the opening project of Guangdong Provincial key Laboratory of Information Security Technology (2017B030314131).

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Authors and Affiliations

  1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Xiong Wang, Ruijin Wang & Daniel Adu-Gyamfi

  2. Chengdu University of Technology, Chengdu, 610059, China

    Dongfen Li

  3. Xinjiang University of Finance & Economics, Wulumuqi Shi, China

    Yixin Zhu

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  1. Xiong Wang
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  2. Ruijin Wang
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  3. Dongfen Li
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  4. Daniel Adu-Gyamfi
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  5. Yixin Zhu
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Correspondence to Ruijin Wang.

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Wang, X., Wang, R., Li, D. et al. QCF: Quantum Collaborative Filtering Recommendation Algorithm. Int J Theor Phys 58, 2235–2243 (2019). https://doi.org/10.1007/s10773-019-04114-7

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  • DOI: https://doi.org/10.1007/s10773-019-04114-7

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