{"id":204179,"updated":"2025-01-19T20:18:21.084497+00:00","links":{},"created":"2025-01-19T01:06:28.578871+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00204179","sets":["1164:3696:10077:10126"]},"path":["10126"],"owner":"44499","recid":"204179","title":["Twitterにおけるセレンディピティなユーザ推薦手法の評価"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-03-09"},"_buckets":{"deposit":"1b2ae55f-dff0-4b4c-90a7-b09395769de0"},"_deposit":{"id":"204179","pid":{"type":"depid","value":"204179","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Twitterにおけるセレンディピティなユーザ推薦手法の評価","author_link":["505157","505158","505154","505155","505153","505156"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Twitterにおけるセレンディピティなユーザ推薦手法の評価"},{"subitem_title":"Evaluation on a Serendipitous User Recommendation Method for Twitter","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"SNS活用","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2020-03-09","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"立命館大学院情報理工学研究科"},{"subitem_text_value":"立命館大学情報理工学部"},{"subitem_text_value":"立命館大学情報理工学部"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/204179/files/IPSJ-GN20110012.pdf","label":"IPSJ-GN20110012.pdf"},"date":[{"dateType":"Available","dateValue":"2022-03-09"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-GN20110012.pdf","filesize":[{"value":"1.4 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":"29"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"b35264d3-055e-49e7-a433-202783b7ea1e","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"徐, 哲林"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"周, 娟"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高田, 秀志"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Zhelin, Xu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Juan, Zhou","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hideyuki, Takada","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA1155524X","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":"2188-8744","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Twitter のお薦めユーザシステムでは，ユーザ自身がフォローしているユーザと同じジャンルのユーザが多く推薦される傾向がある．しかし，このようなお薦めユーザは本人が既に知っている可能性が高く，推薦しなくても自身が見つけられるため，ユーザに高い満足度を与えることが難しい．そのため，本研究では，興味のあるものはユーザの Timeline から抽出した名詞に現れると想定した上で，「個人を惹き付ける興味」を推定することによりセレンディピティなユーザを推薦する手法を構築し，評価実験を行った．その結果，ユーザの Timeline から抽出した名詞に基づいて，興味を推定できるだけではなく，提案手法によりセレンディピティなユーザを発見できることとともに，Twitter のお薦めユーザシステムよりセレンディピティに関する性能も向上することが示された．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Twitter's user recommendation system is likely to focus on prediction accuracy, which tends to recommend users matching with people's profiles. However, such a recommendation system can hardly provide users with high satisfaction because they can likely already know such a recommended user or easily find him or her by themselves. In this paper, assuming that some interests appear in nouns extracted from the user's Timeline, we propose a serendipity-oriented user recommendation scheme to predict serendipitous users that are both unexpected and useful to the user by estimating “user's attracting interests”. The evaluation experiment shows not only that user's interests can be estimated based on nouns extracted from user's tweets, but also that serendipitous users can be found by the proposed scheme and the performance measure on serendipity can be improved compared to the Twitter's user recommendation system.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告グループウェアとネットワークサービス（GN）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2020-03-09","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"12","bibliographicVolumeNumber":"2020-GN-110"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}