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

In this paper, we propose a quantum algorithm for recommendation systems which incorporates the contextual information of users to personalized recommendation. The preference is encoded in third-order tensor dimension N can be approximated by truncated singular value decomposition (t-svd) subsample tensor. Unlike classical that reconstructs using t-svd, our obtains recommended product under certain context measuring output state corresponding an approximation user’s dynamic preferences. achieves time complexity $$\mathcal {O}(\sqrt{k}N\mathrm{polylog}(N))$$ , compared counterpart with {O}(kN^3)$$ where k tubal rank.