{"id":"https://openalex.org/W4409657012","doi":"https://doi.org/10.1145/3696410.3714556","title":"Mitigating the Participation Bias by Balancing Extreme Ratings","display_name":"Mitigating the Participation Bias by Balancing Extreme Ratings","publication_year":2025,"publication_date":"2025-04-22","ids":{"openalex":"https://openalex.org/W4409657012","doi":"https://doi.org/10.1145/3696410.3714556"},"language":"en","primary_location":{"id":"doi:10.1145/3696410.3714556","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3696410.3714556","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3696410.3714556","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3696410.3714556","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088344047","display_name":"Yongkang Guo","orcid":"https://orcid.org/0000-0003-2816-4149"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yongkang Guo","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058603769","display_name":"Yuqing Kong","orcid":"https://orcid.org/0000-0002-5901-3004"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqing Kong","raw_affiliation_strings":["School of Computer Science, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066798802","display_name":"J Y Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jialiang Liu","raw_affiliation_strings":["School of Electronics Engineering and Computer Science, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics Engineering and Computer Science, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088344047"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10254253,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1441","last_page":"1455"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11644","display_name":"Spam and Phishing Detection","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5438805222511292}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5438805222511292}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3696410.3714556","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3696410.3714556","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3696410.3714556","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3696410.3714556","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3696410.3714556","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3696410.3714556","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.550000011920929,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5168064793","display_name":null,"funder_award_id":"62372007","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4409657012.pdf","grobid_xml":"https://content.openalex.org/works/W4409657012.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1507344949","https://openalex.org/W1536678703","https://openalex.org/W1946400902","https://openalex.org/W1978037357","https://openalex.org/W1982750819","https://openalex.org/W1986250339","https://openalex.org/W1989972396","https://openalex.org/W1992462783","https://openalex.org/W2001709030","https://openalex.org/W2015731569","https://openalex.org/W2038249651","https://openalex.org/W2039811614","https://openalex.org/W2063963830","https://openalex.org/W2065974896","https://openalex.org/W2072728150","https://openalex.org/W2097436041","https://openalex.org/W2098121414","https://openalex.org/W2103063352","https://openalex.org/W2114068176","https://openalex.org/W2179880778","https://openalex.org/W2211188029","https://openalex.org/W2284279238","https://openalex.org/W2337430557","https://openalex.org/W2592455408","https://openalex.org/W2623499291","https://openalex.org/W2738273842","https://openalex.org/W2741652148","https://openalex.org/W2962900631","https://openalex.org/W2996865329","https://openalex.org/W3000682630","https://openalex.org/W3045217231","https://openalex.org/W3098016509","https://openalex.org/W3101530157","https://openalex.org/W3107968984","https://openalex.org/W3113647249","https://openalex.org/W3164142491","https://openalex.org/W3211664537","https://openalex.org/W4221075420","https://openalex.org/W4287447061","https://openalex.org/W4296610240","https://openalex.org/W4367294547"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Rating":[0],"aggregation":[1,50],"plays":[2],"a":[3,47,66],"crucial":[4],"role":[5],"in":[6,34,75,101],"various":[7],"fields,":[8],"such":[9],"as":[10],"product":[11],"recommendations,":[12],"hotel":[13],"rankings,":[14],"and":[15,92],"teaching":[16],"evaluations.":[17],"However,":[18],"traditional":[19],"averaging":[20],"methods":[21],"can":[22],"be":[23],"affected":[24],"by":[25],"participation":[26,54],"bias,":[27],"where":[28],"some":[29],"raters":[30,59],"do":[31],"not":[32,61],"participate":[33],"the":[35,53,84,89,93,102],"rating":[36,49],"process,":[37],"leading":[38],"to":[39,82],"potential":[40],"distortions.":[41],"In":[42],"this":[43],"paper,":[44],"we":[45],"consider":[46],"robust":[48],"task":[51],"under":[52],"bias.":[55],"We":[56],"assume":[57],"that":[58],"may":[60],"reveal":[62],"their":[63,71],"ratings":[64,91,98],"with":[65],"certain":[67],"probability":[68],"depending":[69],"on":[70],"individual":[72],"ratings,":[73],"resulting":[74],"partially":[76],"observed":[77],"samples.":[78],"Our":[79],"goal":[80],"is":[81],"minimize":[83],"expected":[85],"squared":[86],"loss":[87],"between":[88],"aggregated":[90],"average":[94],"of":[95],"all":[96],"underlying":[97],"(possibly":[99],"unobserved)":[100],"worst-case":[103],"scenario.":[104]},"counts_by_year":[],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}