{"_buckets": {"deposit": "d67b062a-873b-4791-8d5d-dd5216ab35ec"}, "_deposit": {"id": "4290", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4290"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4290", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Query Dependent Ranking for Information Retrieval Based on Query Clustering", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Ranking is the central problem forinformation retrieval (IR), and employingmachine learning techniques to learn the rankingfunction is viewed as a promising approach toIR. In information retrieval, the users’ queriesoften vary a lot from one to another. Howevermost of existing approaches for ranking do notexplicitly take into consideration the fact thatqueries vary significantly along severaldimensions. In this paper we will take intoaccount the diversity of query type by clusteringthe queries. Instead of deriving a single function,this system attempt to develop several rankingfunctions based on the resulting query clusters inthe sense that different queries of the samecluster should have similar characteristics interms of ranking. So, for each query cluster,there will be its associated ranking model. Torank the documents for a new query, the systemfirst find the most suitable cluster for that queryand produce the scoring results depend on thatcluster. The effectiveness of the system will betested on LETOR, publicly available benchmarkdata set."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value": []}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-03"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "9072.pdf", "filesize": [{"value": "148 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 148000.0, "url": {"url": "https://meral.edu.mm/record/4290/files/9072.pdf"}, "version_id": "cd24dd1f-9714-4bc7-9cf0-9ee93a7dab74"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Ninth International Conference On Computer Applications (ICCA 2011)", "subitem_pages": "", "subitem_volume": ""}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "", "subitem_c_date": "", "subitem_conference_title": "", "subitem_part": "", "subitem_place": "", "subitem_session": "", "subitem_website": ""}]}, "item_1583103211336": {"attribute_name": "Books/reports/chapters", "attribute_value_mlt": [{"subitem_book_title": "", "subitem_isbn": "", "subitem_pages": "", "subitem_place": "", "subitem_publisher": ""}]}, "item_1583103233624": {"attribute_name": "Thesis/dissertations", "attribute_value_mlt": [{"subitem_awarding_university": "", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Lwin, Pwint Hay Mar"}, {"subitem_authors_fullname": "Kham, Nang Saing Moon"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Article"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2011-05-05"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/222"}, "item_title": "Query Dependent Ranking for Information Retrieval Based on Query Clustering", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004290", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-03"}, "publish_date": "2019-07-03", "publish_status": "0", "recid": "4290", "relation": {}, "relation_version_is_last": true, "title": ["Query Dependent Ranking for Information Retrieval Based on Query Clustering"], "weko_shared_id": -1}