{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00227777","sets":["1164:3500:11130:11328"]},"path":["11328"],"owner":"44499","recid":"227777","title":["MultArtRec: A Multimodal Neural Topic Model for Integrating Image and Textual Features in Artwork Recommendation"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-09-14"},"_buckets":{"deposit":"116aab48-92a5-4a95-a314-0a10881bdfea"},"_deposit":{"id":"227777","pid":{"type":"depid","value":"227777","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"MultArtRec: A Multimodal Neural Topic Model for Integrating Image and Textual Features in Artwork Recommendation","author_link":["607232","607237","607235","607233","607234","607236"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"MultArtRec: A Multimodal Neural Topic Model for Integrating Image and Textual Features in Artwork Recommendation"},{"subitem_title":"MultArtRec: A Multimodal Neural Topic Model for Integrating Image and Textual Features in Artwork Recommendation","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2023-09-14","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Engineering, Ritsumeikan University"},{"subitem_text_value":"College of Information Science and Engineering, Ritsumeikan University"},{"subitem_text_value":"College of Information Science and Engineering, Ritsumeikan University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Engineering, Ritsumeikan University","subitem_text_language":"en"},{"subitem_text_value":"College of Information Science and Engineering, Ritsumeikan University","subitem_text_language":"en"},{"subitem_text_value":"College of Information Science and Engineering, Ritsumeikan University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/227777/files/IPSJ-IFAT23152005.pdf","label":"IPSJ-IFAT23152005.pdf"},"date":[{"dateType":"Available","dateValue":"2025-09-14"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-IFAT23152005.pdf","filesize":[{"value":"1.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"39"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"8a65d5a5-b6fd-4d45-96b8-42373cd541f7","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Jiayun, Wang"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akira, Maeda"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kyoji, Kawagoe"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Jiayun, Wang","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akira, Maeda","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kyoji, Kawagoe","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10114171","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8884","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Multimodal models have demonstrated remarkable success in the domains of image processing and natural language processing. Recently, their significance has also been acknowledged within recommendation systems. In many cases, the recommendation systems perform better when utilizing multimodal features to construct item embeddings, rather than utilizing individual text or image models. Consequently, research in this field has shifted its focus towards effectively combining multimodal features and accurately embedding items. Our study specifically concentrates on artwork recommendation. In artwork recommendation, the textual data such as titles and descriptions notably influence users' preferences. Our research approach involves constructing multimodal embeddings of artworks by integrating both images and titles as a fundamental step.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Multimodal models have demonstrated remarkable success in the domains of image processing and natural language processing. Recently, their significance has also been acknowledged within recommendation systems. In many cases, the recommendation systems perform better when utilizing multimodal features to construct item embeddings, rather than utilizing individual text or image models. Consequently, research in this field has shifted its focus towards effectively combining multimodal features and accurately embedding items. Our study specifically concentrates on artwork recommendation. In artwork recommendation, the textual data such as titles and descriptions notably influence users' preferences. Our research approach involves constructing multimodal embeddings of artworks by integrating both images and titles as a fundamental step.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"3","bibliographic_titles":[{"bibliographic_title":"研究報告情報基礎とアクセス技術（IFAT）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-09-14","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"2023-IFAT-152"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:27:01.799456+00:00","updated":"2025-01-19T12:04:30.960168+00:00","id":227777}