Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93290
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Wang, X | en_US |
dc.creator | Gu, L | en_US |
dc.creator | Lee, HW | en_US |
dc.creator | Zhang, G | en_US |
dc.date.accessioned | 2022-06-15T03:42:39Z | - |
dc.date.available | 2022-06-15T03:42:39Z | - |
dc.identifier.issn | 1570-0755 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93290 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.rights | © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 | en_US |
dc.rights | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11128-021-03131-y | en_US |
dc.subject | Context-aware recommendation systems | en_US |
dc.subject | Quantum Fourier transform | en_US |
dc.subject | Quantum singular value estimation | en_US |
dc.subject | T-svd | en_US |
dc.title | Quantum context-aware recommendation systems based on tensor singular value decomposition | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 20 | en_US |
dc.identifier.issue | 5 | en_US |
dc.identifier.doi | 10.1007/s11128-021-03131-y | en_US |
dcterms.abstract | 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. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Quantum information processing, May 2021, v. 20, no. 5, 190 | en_US |
dcterms.isPartOf | Quantum information processing | en_US |
dcterms.issued | 2021-05 | - |
dc.identifier.scopus | 2-s2.0-85106901077 | - |
dc.identifier.artn | 190 | en_US |
dc.description.validate | 202206 bcfc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | AMA-0049 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Shenzhen Fundamental Research Fund | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 53013984 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Wang_Quantum_Context-Aware_Recommendation.pdf | Pre-Published version | 1.38 MB | Adobe PDF | View/Open |
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