{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00101725","sets":["581:7397:7609"]},"path":["7609"],"owner":"11","recid":"101725","title":["Algorithms and Techniques for Proactive Search"],"pubdate":{"attribute_name":"公開日","attribute_value":"2014-06-15"},"_buckets":{"deposit":"60c031f2-ca12-47d8-a2a4-4addcf05ad74"},"_deposit":{"id":"101725","pid":{"type":"depid","value":"101725","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"Algorithms and Techniques for Proactive Search","author_link":["11341","11339","11340","11342","11338","11343"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Algorithms and Techniques for Proactive Search"},{"subitem_title":"Algorithms and Techniques for Proactive Search","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集：Applications and the Internet in Conjunction with Main Topics of COMPSAC 2013] proactive search, neighborhoods of similarity, recommendation algorithms","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2014-06-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Missouri University of Science and Technology"},{"subitem_text_value":"Missouri University of Science and Technology"},{"subitem_text_value":"Missouri University of Science and Technology"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Missouri University of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Missouri University of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Missouri University of Science and Technology","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/101725/files/IPSJ-JNL5506003.pdf","label":"IPSJ-JNL5506003"},"date":[{"dateType":"Available","dateValue":"2016-06-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL5506003.pdf","filesize":[{"value":"222.7 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"f0de23a4-68bc-4be1-945b-faf829098b01","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2014 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"C.ShaunWagner"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"SahraSedighSarvestani"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"AliR.Hurson"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"C., ShaunWagner","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Sahra, SedighSarvestani","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ali, R.Hurson","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":"While search engines have demonstrated improvements in both speed and accuracy, their response time is prohibitively long for applications that require immediate and accurate responses to search queries. Examples include identification of multimedia resources related to the subject matter of a particular class, as it is in session. This paper begins with a survey of collaborative recommendation and prediction algorithms, each of which applies a different method to predict future search engine usage based on the past history of a search engine user. To address the shortcomings identified in existing techniques, we propose a proactive search approach that identifies resources likely to be of interest to the user without requiring a query. The approach is contingent on accurate determination of similarity, which we achieve with local alignment and output-based refinement of similarity neighborhoods. We demonstrate our proposed approach with trials on real-world search engine data. The results support our hypothesis that a majority of users exhibit search engine usage behavior that is predictable, allowing a proactive search engine to bypass the common query-response model and immediately deliver a list of resources of interest to the user.\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.22(2014) No.3 (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.22.425\n------------------------------","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"While search engines have demonstrated improvements in both speed and accuracy, their response time is prohibitively long for applications that require immediate and accurate responses to search queries. Examples include identification of multimedia resources related to the subject matter of a particular class, as it is in session. This paper begins with a survey of collaborative recommendation and prediction algorithms, each of which applies a different method to predict future search engine usage based on the past history of a search engine user. To address the shortcomings identified in existing techniques, we propose a proactive search approach that identifies resources likely to be of interest to the user without requiring a query. The approach is contingent on accurate determination of similarity, which we achieve with local alignment and output-based refinement of similarity neighborhoods. We demonstrate our proposed approach with trials on real-world search engine data. The results support our hypothesis that a majority of users exhibit search engine usage behavior that is predictable, allowing a proactive search engine to bypass the common query-response model and immediately deliver a list of resources of interest to the user.\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.22(2014) No.3 (online)\nDOI　http://dx.doi.org/10.2197/ipsjjip.22.425\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":"2014-06-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"6","bibliographicVolumeNumber":"55"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":101725,"updated":"2025-01-20T06:45:54.777177+00:00","links":{},"created":"2025-01-18T23:47:12.965340+00:00"}