Collaborative Filtering Recommendation on Users’ Interest Sequences
Publication Date
May 19, 2016
Journal
PLOS ONE
Authors
Weijie Cheng, Guisheng Yin, Yuxin Dong, Hongbin Dong, et al
Volume
11
Issue
5
Pages
e0155739
DOI
https://dx.plos.org/10.1371/journal.pone.0155739
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0155739
Scopus
84973638167
Mendeley
http://www.mendeley.com/research/collaborative-filtering-recommendation-users-interest-sequences
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Mendeley | Further Information

{"title"=>"Collaborative filtering recommendation on users' interest sequences", "type"=>"journal", "authors"=>[{"first_name"=>"Weijie", "last_name"=>"Cheng", "scopus_author_id"=>"55105398100"}, {"first_name"=>"Guisheng", "last_name"=>"Yin", "scopus_author_id"=>"55817240200"}, {"first_name"=>"Yuxin", "last_name"=>"Dong", "scopus_author_id"=>"15118927900"}, {"first_name"=>"Hongbin", "last_name"=>"Dong", "scopus_author_id"=>"14053990800"}, {"first_name"=>"Wansong", "last_name"=>"Zhang", "scopus_author_id"=>"55361689000"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84973638167", "pmid"=>"27195787", "doi"=>"10.1371/journal.pone.0155739", "pui"=>"610470979", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "sgr"=>"84973638167"}, "id"=>"1f9adebe-abc4-3748-9241-9c165817e636", "abstract"=>"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. [ABSTRACT FROM AUTHOR]", "link"=>"http://www.mendeley.com/research/collaborative-filtering-recommendation-users-interest-sequences", "reader_count"=>17, "reader_count_by_academic_status"=>{"Student > Ph. D. Student"=>6, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>3}, "reader_count_by_user_role"=>{"Student > Ph. D. Student"=>6, "Student > Postgraduate"=>2, "Student > Master"=>5, "Other"=>1, "Student > Bachelor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Arts and Humanities"=>1, "Business, Management and Accounting"=>2, "Computer Science"=>12}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Computer Science"=>{"Computer Science"=>12}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Ireland"=>1, "Germany"=>1}, "group_count"=>2}

Scopus | Further Information

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