Quantum context-aware recommendation systems based on tensor singular value decomposition

Xiaoqiang Wang, Lejia Gu, Heung wing Lee, Guofeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


In this paper, we propose a quantum algorithm for recommendation systems which incorporates the contextual information of users to the personalized recommendation. The preference information of users is encoded in a third-order tensor of dimension N which can be approximated by the truncated tensor singular value decomposition (t-svd) of the subsample tensor. Unlike the classical algorithm that reconstructs the approximated preference tensor using truncated t-svd, our quantum algorithm obtains the recommended product under certain context by measuring the output quantum state corresponding to an approximation of a user’s dynamic preferences. The algorithm achieves the time complexity O(kNpolylog(N)), compared to the classical counterpart with complexity O(kN3) , where k is the truncated tubal rank.

Original languageEnglish
Article number190
Pages (from-to)1-32
Number of pages32
JournalQuantum Information Processing
Issue number5
Publication statusE-pub ahead of print - 26 May 2021


  • Context-aware recommendation systems
  • Quantum Fourier transform
  • Quantum singular value estimation
  • t-svd

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Statistical and Nonlinear Physics
  • Theoretical Computer Science
  • Signal Processing
  • Modelling and Simulation
  • Electrical and Electronic Engineering


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