Quantum recommendation systems

A recommendation system uses the past purchases or ratings of n products by a group of m users, in order to provide personalized recommendations to individual users. The information is modeled as an m \times n preference matrix which is assumed to have a good rank-k approximation, for a small consta...

Full description

Saved in:
Bibliographic Details
Main Authors: Kerenidis, Iordanis, Prakash, Anupam
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2018
Subjects:
Online Access:https://hdl.handle.net/10356/89347
http://hdl.handle.net/10220/46208
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-89347
record_format dspace
spelling sg-ntu-dr.10356-893472023-02-28T19:36:11Z Quantum recommendation systems Kerenidis, Iordanis Prakash, Anupam School of Physical and Mathematical Sciences Centre for Quantum Technologies Quantum Machine Learning Recommendation Systems DRNTU::Science::Physics A recommendation system uses the past purchases or ratings of n products by a group of m users, in order to provide personalized recommendations to individual users. The information is modeled as an m \times n preference matrix which is assumed to have a good rank-k approximation, for a small constant k. In this work, we present a quantum algorithm for recommendation systems that has running time O(\text{poly}(k)\text{polylog}(mn)). All known classical algorithms for recommendation systems that work through reconstructing an approximation of the preference matrix run in time polynomial in the matrix dimension. Our algorithm provides good recommendations by sampling efficiently from an approximation of the preference matrix, without reconstructing the entire matrix. For this, we design an efficient quantum procedure to project a given vector onto the row space of a given matrix. This is the first algorithm for recommendation systems that runs in time polylogarithmic in the dimensions of the matrix and provides an example of a quantum machine learning algorithm for a real world application. NRF (Natl Research Foundation, S’pore) Published version 2018-10-03T06:57:50Z 2019-12-06T17:23:28Z 2018-10-03T06:57:50Z 2019-12-06T17:23:28Z 2017 Journal Article Kerenidis, I., & Prakash, A. (2017). Quantum recommendation systems. Leibniz International Proceedings in Informatics, 67, 49-. doi:10.4230/LIPIcs.ITCS.2017.49 https://hdl.handle.net/10356/89347 http://hdl.handle.net/10220/46208 10.4230/LIPIcs.ITCS.2017.49 en Leibniz International Proceedings in Informatics © 2017 Iordanis Kerenidis and Anupam Prakash; licensed under Creative Commons License CC-BY. 21 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Quantum Machine Learning
Recommendation Systems
DRNTU::Science::Physics
spellingShingle Quantum Machine Learning
Recommendation Systems
DRNTU::Science::Physics
Kerenidis, Iordanis
Prakash, Anupam
Quantum recommendation systems
description A recommendation system uses the past purchases or ratings of n products by a group of m users, in order to provide personalized recommendations to individual users. The information is modeled as an m \times n preference matrix which is assumed to have a good rank-k approximation, for a small constant k. In this work, we present a quantum algorithm for recommendation systems that has running time O(\text{poly}(k)\text{polylog}(mn)). All known classical algorithms for recommendation systems that work through reconstructing an approximation of the preference matrix run in time polynomial in the matrix dimension. Our algorithm provides good recommendations by sampling efficiently from an approximation of the preference matrix, without reconstructing the entire matrix. For this, we design an efficient quantum procedure to project a given vector onto the row space of a given matrix. This is the first algorithm for recommendation systems that runs in time polylogarithmic in the dimensions of the matrix and provides an example of a quantum machine learning algorithm for a real world application.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Kerenidis, Iordanis
Prakash, Anupam
format Article
author Kerenidis, Iordanis
Prakash, Anupam
author_sort Kerenidis, Iordanis
title Quantum recommendation systems
title_short Quantum recommendation systems
title_full Quantum recommendation systems
title_fullStr Quantum recommendation systems
title_full_unstemmed Quantum recommendation systems
title_sort quantum recommendation systems
publishDate 2018
url https://hdl.handle.net/10356/89347
http://hdl.handle.net/10220/46208
_version_ 1759856148928790528