Algorithms and Techniques for Proactive Search

• Item type: Journal(1)
• 公開日: 2014-06-15
• タイトル: Algorithms and Techniques for Proactive Search
• タイトル / 言語: en
• タイトル: Algorithms and Techniques for Proactive Search
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：Applications and the Internet in Conjunction with Main Topics of COMPSAC 2013] proactive search, neighborhoods of similarity, recommendation algorithms
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Missouri University of Science and Technology
• 著者所属: Missouri University of Science and Technology
• 著者所属: Missouri University of Science and Technology
• 著者所属(英): en
• 著者所属(英): Missouri University of Science and Technology
• 著者所属(英): en
• 著者所属(英): Missouri University of Science and Technology
• 著者所属(英): en
• 著者所属(英): Missouri University of Science and Technology
• 著者名: C.ShaunWagner; SahraSedighSarvestani; AliR.Hurson
• 著者名(英): C., ShaunWagner; Sahra, SedighSarvestani; Ali, R.Hurson
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.22(2014) No.3 (online); DOI http://dx.doi.org/10.2197/ipsjjip.22.425; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.22(2014) No.3 (online); DOI http://dx.doi.org/10.2197/ipsjjip.22.425; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 55, 号 6, 発行日 2014-06-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764