{"_buckets": {"deposit": "7a4980b0-8bb6-4800-b23d-e898727663c3"}, "_deposit": {"created_by": 23, "id": "2001300", "owners": [23], "pid": {"revision_id": 0, "type": "depid", "value": "2001300"}, "status": "published"}, "_oai": {"id": "oai:repository.nii.ac.jp:02001300", "sets": ["1697763112205"]}, "author_link": [], "control_number": "2001300", "item_30002_conference34": {"attribute_name": "会議記述", "attribute_value_mlt": [{"subitem_conference_country": "JPN", "subitem_conference_date": {"subitem_conference_date_language": "en", "subitem_conference_end_day": "15", "subitem_conference_end_month": "12", "subitem_conference_end_year": "2023", "subitem_conference_period": "December 12-15, 2023", "subitem_conference_start_day": "12", "subitem_conference_start_month": "12", "subitem_conference_start_year": "2023"}, "subitem_conference_names": [{"subitem_conference_name": "NTCIR-17 Conference", "subitem_conference_name_language": "en"}], "subitem_conference_sequence": "17", "subitem_conference_sponsors": [{"subitem_conference_sponsor": "National Institute of Informatics", "subitem_conference_sponsor_language": "en"}], "subitem_conference_venues": [{"subitem_conference_venue": "National Institute of Informatics", "subitem_conference_venue_language": "en"}]}]}, "item_30002_creator2": {"attribute_name": "作成者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Fumian Chen", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "Hui Fang", "creatorNameLang": "en"}]}]}, "item_30002_date11": {"attribute_name": "日付", "attribute_value_mlt": [{"subitem_date_issued_datetime": "2023-12-12", "subitem_date_issued_type": "Issued"}]}, "item_30002_description9": {"attribute_name": "内容記述", "attribute_value_mlt": [{"subitem_description": "Providing relevant, diverse, and fair results is crucial for informationretrieval systems. It has attracted more and more attentionbecause of issues caused by traditional relevance-centric retrieval systems.These issues include the problem of echo chambers and theincreasingly polarized online communities. Therefore, we participatedin the NTCIR-17 FairWeb-1 Task to provide group fairness toresearchers, movies, and YouTube content and submitted five runs.The runs are based on a recently proposed fair ranking framework,DLF. The experimental results demonstrate that, in many cases, DLF can improve fairness while maintaining relevance but stillneeds more exploration for ordinal fairness groups and documentswith longer text. 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UDInfoLab at the NTCIR-17 FairWeb-1 Task
https://doi.org/10.20736/0002001300
https://doi.org/10.20736/0002001300