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Adaptive personalized recommendation based on adaptive learning

Authors:

Qing-Hua Zheng,

Lin ChenAuthors Info & Claims

Neurocomputing, Volume 74, Issue 11

Pages 1848 - 1858

https://doi.org/10.1016/j.neucom.2010.07.034

Published: 01 May 2011 Publication History

Abstract

Collaborative filtering has been widely applied in many fields in recent years due to the increase in web-based activities such as e-commerce and online content distribution. Current collaborative filtering techniques such as correlation-based, SVD-based and supervised learning-based approaches provide good accuracy, but are computationally very expensive and can only be deployed in static off-line settings, where the known rating information does not change with time. However, a number of practical scenarios require dynamic adaptive collaborative filtering that can allow new users, items and ratings to enter the system at a rapid rate. In this paper, we consider a novel adaptive personalized recommendation based on adaptive learning. Fast adaptive learning runs through all the aspects of the proposed approach, including training, prediction and updating. Empirical evaluation of our approach on Movielens dataset demonstrates that it is possible to obtain accuracy comparable to that of the correlation-based, SVD-based and supervised learning-based approaches at a much lower computational cost.

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Cited By

Deng JWang YGuo JDeng YGao JPark Y(2020)A similarity measure based on Kullback–Leibler divergence for collaborative filtering in sparse dataJournal of Information Science10.1177/016555151880818845:5(656-675)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1177/0165551518808188
Ren XSong ME HSong J(2017)Context-aware probabilistic matrix factorization modeling for point-of-interest recommendationNeurocomputing10.1016/j.neucom.2017.02.005241:C(38-55)Online publication date: 7-Jun-2017
https://dl.acm.org/doi/10.1016/j.neucom.2017.02.005
Li HCui JShen BMa J(2016)An intelligent movie recommendation system through group-level sentiment analysis in microblogsNeurocomputing10.1016/j.neucom.2015.09.134210:C(164-173)Online publication date: 19-Oct-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.09.134
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Adaptive personalized recommendation based on adaptive learning
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cover image Neurocomputing

Neurocomputing Volume 74, Issue 11

May, 2011

259 pages

ISSN:0925-2312

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2011.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 May 2011

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Cited By

Deng JWang YGuo JDeng YGao JPark Y(2020)A similarity measure based on Kullback–Leibler divergence for collaborative filtering in sparse dataJournal of Information Science10.1177/016555151880818845:5(656-675)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1177/0165551518808188
Ren XSong ME HSong J(2017)Context-aware probabilistic matrix factorization modeling for point-of-interest recommendationNeurocomputing10.1016/j.neucom.2017.02.005241:C(38-55)Online publication date: 7-Jun-2017
https://dl.acm.org/doi/10.1016/j.neucom.2017.02.005
Li HCui JShen BMa J(2016)An intelligent movie recommendation system through group-level sentiment analysis in microblogsNeurocomputing10.1016/j.neucom.2015.09.134210:C(164-173)Online publication date: 19-Oct-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.09.134
Wang XWen JLuo FZhou WRen H(2015)Personalized Recommendation System Based on Support Vector Machine and Particle Swarm OptimizationKnowledge Science, Engineering and Management10.1007/978-3-319-25159-2_44(489-495)Online publication date: 28-Oct-2015
https://dl.acm.org/doi/10.1007/978-3-319-25159-2_44
Gan MJiang R(2013)Constructing a user similarity network to remove adverse influence of popular objects for personalized recommendationExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.01.00440:10(4044-4053)Online publication date: 1-Aug-2013
https://dl.acm.org/doi/10.1016/j.eswa.2013.01.004

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