{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00213299","sets":["581:10433:10443"]},"path":["10443"],"owner":"44499","recid":"213299","title":["Predicting Next-use Mobile Apps Using App Semantic Representations "],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-10-15"},"_buckets":{"deposit":"86861a1c-c3ac-456b-9b6f-85836d17bf00"},"_deposit":{"id":"213299","pid":{"type":"depid","value":"213299","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Predicting Next-use Mobile Apps Using App Semantic Representations ","author_link":["545583","545586","545584","545585","545588","545589","545587","545590"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Predicting Next-use Mobile Apps Using App Semantic Representations "},{"subitem_title":"Predicting Next-use Mobile Apps Using App Semantic Representations ","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集：ユビキタスコンピューティングシステム（X）] smartphone, mobile apps, next-use app prediction, pattern recognition","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2021-10-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, Osaka University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/213299/files/IPSJ-JNL6210004.pdf","label":"IPSJ-JNL6210004.pdf"},"date":[{"dateType":"Available","dateValue":"2023-10-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6210004.pdf","filesize":[{"value":"1.4 MB"}],"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":"e0755cc7-8551-4ac5-a6d7-484d65492bca","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Cheng, Chen"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuya, Maekawa"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Daichi, Amagata"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takahiro, Hara"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Cheng, Chen","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takuya, Maekawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Daichi, Amagata","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takahiro, Hara","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":"Using the app usage history of a target user as a basis, this study proposes a novel method for predicting next-use mobile apps of the user that can assist the user in selecting an app from a list of installed apps. The proposed method is designed to train a next-use app prediction model using semantic representations of the usage histories of other users (source users) to deal with the user and app cold-start problems of an app prediction system in which training data from a target user beginning to use the system and training data related to newly installed or released apps are considered to be insufficient. We predict the usage of apps by a target user by leveraging the semantic similarities between the apps that are installed on the smartphones of the source users and the apps that are installed on the smartphone of a target user, permitting us to predict next-use apps regardless of the apps installed in the target user's smartphone. We evaluate our method using the actual app usage data collected from 100 participants over a period of approximately 70 days with 300,000 app usage histories.\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.29(2021) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.29.597\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Using the app usage history of a target user as a basis, this study proposes a novel method for predicting next-use mobile apps of the user that can assist the user in selecting an app from a list of installed apps. The proposed method is designed to train a next-use app prediction model using semantic representations of the usage histories of other users (source users) to deal with the user and app cold-start problems of an app prediction system in which training data from a target user beginning to use the system and training data related to newly installed or released apps are considered to be insufficient. We predict the usage of apps by a target user by leveraging the semantic similarities between the apps that are installed on the smartphones of the source users and the apps that are installed on the smartphone of a target user, permitting us to predict next-use apps regardless of the apps installed in the target user's smartphone. We evaluate our method using the actual app usage data collected from 100 participants over a period of approximately 70 days with 300,000 app usage histories.\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.29(2021) (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.29.597\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":"2021-10-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"62"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:14:10.988833+00:00","updated":"2025-01-19T17:12:28.358097+00:00","id":213299}