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Collaborative Filtering Recommendation on Users’ Interest Sequences

As an important factor for improving recommendations, time information has been introduced to model users’ dynamic preferences in many papers. However, the sequence of users’ behaviour is rarely studied in recommender systems. Due to the users’ unique behavior evolution patterns and personalized int...

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Detalles Bibliográficos
Autores principales: Cheng, Weijie, Yin, Guisheng, Dong, Yuxin, Dong, Hongbin, Zhang, Wansong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4873175/
https://www.ncbi.nlm.nih.gov/pubmed/27195787
http://dx.doi.org/10.1371/journal.pone.0155739
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author Cheng, Weijie
Yin, Guisheng
Dong, Yuxin
Dong, Hongbin
Zhang, Wansong
author_facet Cheng, Weijie
Yin, Guisheng
Dong, Yuxin
Dong, Hongbin
Zhang, Wansong
author_sort Cheng, Weijie
collection PubMed
description As an important factor for improving recommendations, time information has been introduced to model users’ dynamic preferences in many papers. However, the sequence of users’ behaviour is rarely studied in recommender systems. Due to the users’ unique behavior evolution patterns and personalized interest transitions among items, users’ similarity in sequential dimension should be introduced to further distinguish users’ preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users’ interest sequences (IS) that rank users’ ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users’ longest common sub-IS (LCSIS) and the count of users’ total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users’ IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users’ preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.
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spelling pubmed-48731752016-06-09 Collaborative Filtering Recommendation on Users’ Interest Sequences Cheng, Weijie Yin, Guisheng Dong, Yuxin Dong, Hongbin Zhang, Wansong PLoS One Research Article As an important factor for improving recommendations, time information has been introduced to model users’ dynamic preferences in many papers. However, the sequence of users’ behaviour is rarely studied in recommender systems. Due to the users’ unique behavior evolution patterns and personalized interest transitions among items, users’ similarity in sequential dimension should be introduced to further distinguish users’ preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users’ interest sequences (IS) that rank users’ ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users’ longest common sub-IS (LCSIS) and the count of users’ total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users’ IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users’ preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction. Public Library of Science 2016-05-19 /pmc/articles/PMC4873175/ /pubmed/27195787 http://dx.doi.org/10.1371/journal.pone.0155739 Text en © 2016 Cheng et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Cheng, Weijie
Yin, Guisheng
Dong, Yuxin
Dong, Hongbin
Zhang, Wansong
Collaborative Filtering Recommendation on Users’ Interest Sequences
title Collaborative Filtering Recommendation on Users’ Interest Sequences
title_full Collaborative Filtering Recommendation on Users’ Interest Sequences
title_fullStr Collaborative Filtering Recommendation on Users’ Interest Sequences
title_full_unstemmed Collaborative Filtering Recommendation on Users’ Interest Sequences
title_short Collaborative Filtering Recommendation on Users’ Interest Sequences
title_sort collaborative filtering recommendation on users’ interest sequences
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4873175/
https://www.ncbi.nlm.nih.gov/pubmed/27195787
http://dx.doi.org/10.1371/journal.pone.0155739
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