{"links":{},"id":158120,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00158120","sets":["581:8417:8420"]},"path":["8420"],"owner":"11","recid":"158120","title":["An Extension for Bounded-SVD - A Matrix Factorization Method with Bound Constraints for Recommender Systems"],"pubdate":{"attribute_name":"公開日","attribute_value":"2016-03-15"},"_buckets":{"deposit":"12abcf78-76c5-4b56-845a-155a17ec9658"},"_deposit":{"id":"158120","pid":{"type":"depid","value":"158120","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"An Extension for Bounded-SVD - A Matrix Factorization Method with Bound Constraints for Recommender Systems","author_link":["301167","301162","301166","301164","301165","301163"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"An Extension for Bounded-SVD - A Matrix Factorization Method with Bound Constraints for Recommender Systems"},{"subitem_title":"An Extension for Bounded-SVD - A Matrix Factorization Method with Bound Constraints for Recommender Systems","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集：学生・若手研究者論文] collaborative filtering, matrix factorization, bound constraints, recommender systems, stochastic gradient descent","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2016-03-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science & Engineering, Ritsumeikan University"},{"subitem_text_value":"College of Information Science & Engineering, Ritsumeikan University"},{"subitem_text_value":"College of Information Science & Engineering, Ritsumeikan University"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science & Engineering, Ritsumeikan University","subitem_text_language":"en"},{"subitem_text_value":"College of Information Science & Engineering, Ritsumeikan University","subitem_text_language":"en"},{"subitem_text_value":"College of Information Science & Engineering, Ritsumeikan University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"publish_status":"0","weko_shared_id":11,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/158120/files/IPSJ-JNL5703009.pdf","label":"IPSJ-JNL5703009.pdf"},"date":[{"dateType":"Available","dateValue":"2018-03-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL5703009.pdf","filesize":[{"value":"818.8 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"5"},{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"5a1d999b-4930-4881-abb7-08cdde18e9ec","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2016 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Bang, Hai,Le"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazuki, Mori"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ruck, Thawonmas"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Bang, Hai Le","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazuki, Mori","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ruck, Thawonmas","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"In this paper, we introduce a new extension for bounded-SVD, i.e., a matrix factorization (MF) method with bound constraints for recommender system. In bounded-SVD, the bound constraints are included in the objective function so that not only the estimation errors are minimized but the constraints are also taken into account during the optimization process. Our previous results on major real-world recommender system datasets showed that bounded-SVD outperformed an existing MF method with bound constraints, BMF, and it is also faster and simpler to implement than BMF. However, an issue of bounded-SVD is that it does not take into account the bias effects in given data. In order to overcome this issue, we propose an extension of bounded-SVD: bounded-SVD bias. Bounded-SVD bias takes into account the rating biases of users and items - known to reside in recommender system data. The experiment results show that the bias extension can improve the performance of bounded-SVD in most cases.\n\\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.24(2016) No.2 (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.24.314\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, we introduce a new extension for bounded-SVD, i.e., a matrix factorization (MF) method with bound constraints for recommender system. In bounded-SVD, the bound constraints are included in the objective function so that not only the estimation errors are minimized but the constraints are also taken into account during the optimization process. Our previous results on major real-world recommender system datasets showed that bounded-SVD outperformed an existing MF method with bound constraints, BMF, and it is also faster and simpler to implement than BMF. However, an issue of bounded-SVD is that it does not take into account the bias effects in given data. In order to overcome this issue, we propose an extension of bounded-SVD: bounded-SVD bias. Bounded-SVD bias takes into account the rating biases of users and items - known to reside in recommender system data. The experiment results show that the bias extension can improve the performance of bounded-SVD in most cases.\n\\n------------------------------\nThis is a preprint of an article intended for publication Journal of\nInformation Processing(JIP). This preprint should not be cited. This\narticle should be cited as: Journal of Information Processing Vol.24(2016) No.2 (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.24.314\n------------------------------","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2016-03-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"57"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-19T00:31:53.610504+00:00","updated":"2025-01-20T06:55:18.405346+00:00"}