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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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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. |
format | Online Article Text |
id | pubmed-4873175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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