{"id":"https://openalex.org/W4393118480","doi":"https://doi.org/10.1145/3637528.3671565","title":"Understanding the Ranking Loss for Recommendation with Sparse User Feedback","display_name":"Understanding the Ranking Loss for Recommendation with Sparse User Feedback","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4393118480","doi":"https://doi.org/10.1145/3637528.3671565"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671565","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671565","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671565","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671565","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043696495","display_name":"Zhutian Lin","orcid":"https://orcid.org/0009-0004-9745-7820"},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhutian Lin","raw_affiliation_strings":["Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007735734","display_name":"Junwei Pan","orcid":"https://orcid.org/0000-0003-3682-2738"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junwei Pan","raw_affiliation_strings":["Tencent Inc., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Inc., Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036962057","display_name":"Shangyu Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shangyu Zhang","raw_affiliation_strings":["Tencent Inc., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Inc., Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059586503","display_name":"Ximei Wang","orcid":"https://orcid.org/0009-0007-3766-0300"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ximei Wang","raw_affiliation_strings":["Tencent Inc., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Inc., Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101600503","display_name":"Xi Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I3131625388","display_name":"University Town of Shenzhen","ror":"https://ror.org/05f5j6225","country_code":"CN","type":"education","lineage":["https://openalex.org/I3131625388"]},{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Xiao","raw_affiliation_strings":["Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China","institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002918925","display_name":"Shudong Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shudong Huang","raw_affiliation_strings":["Tencent Inc., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Inc., Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100441098","display_name":"Lei Xiao","orcid":"https://orcid.org/0000-0003-0246-2762"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Xiao","raw_affiliation_strings":["Tencent Inc., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Inc., Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037387576","display_name":"Jie Jiang","orcid":"https://orcid.org/0000-0001-9658-5127"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Jiang","raw_affiliation_strings":["Tencent Inc., Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Inc., Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5043696495"],"corresponding_institution_ids":["https://openalex.org/I3131625388","https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":3.5128,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.92172068,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5409","last_page":"5418"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9929999709129333,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9926999807357788,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9790999889373779,"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/ranking","display_name":"Ranking (information retrieval)","score":0.8720996975898743},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.7003002762794495},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6643357276916504},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.49619394540786743},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.48935121297836304},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.487617164850235},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4820197820663452},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4509158730506897},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3727540373802185},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35493817925453186},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.33148545026779175},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1789809763431549}],"concepts":[{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.8720996975898743},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.7003002762794495},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6643357276916504},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.49619394540786743},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.48935121297836304},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.487617164850235},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4820197820663452},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4509158730506897},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3727540373802185},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35493817925453186},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33148545026779175},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1789809763431549},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3637528.3671565","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671565","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671565","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2403.14144","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2403.14144","pdf_url":"https://arxiv.org/pdf/2403.14144","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1145/3637528.3671565","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3671565","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3671565","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4393118480.pdf"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1949314668","https://openalex.org/W1976517433","https://openalex.org/W1985759455","https://openalex.org/W1986307354","https://openalex.org/W2030978506","https://openalex.org/W2047221353","https://openalex.org/W2062806705","https://openalex.org/W2074694452","https://openalex.org/W2076618162","https://openalex.org/W2091158010","https://openalex.org/W2108862644","https://openalex.org/W2143331230","https://openalex.org/W2167432060","https://openalex.org/W2295739661","https://openalex.org/W2548570154","https://openalex.org/W2572651649","https://openalex.org/W2604662567","https://openalex.org/W2788490371","https://openalex.org/W2793768763","https://openalex.org/W2810050051","https://openalex.org/W2963351448","https://openalex.org/W2964182926","https://openalex.org/W2973171206","https://openalex.org/W3093945404","https://openalex.org/W3132126111","https://openalex.org/W3208543775","https://openalex.org/W3212850139","https://openalex.org/W4284706321","https://openalex.org/W4290928026","https://openalex.org/W4385568035","https://openalex.org/W4387848868","https://openalex.org/W4392489983","https://openalex.org/W4393159871"],"related_works":["https://openalex.org/W3127142483","https://openalex.org/W4385565564","https://openalex.org/W2138488530","https://openalex.org/W2898073868","https://openalex.org/W2971071571","https://openalex.org/W2798835721","https://openalex.org/W2387658907","https://openalex.org/W2922169395","https://openalex.org/W2385796165","https://openalex.org/W25098770"],"abstract_inverted_index":{"Click-through":[0],"rate":[1],"(CTR)":[2],"prediction":[3,29],"is":[4,60,83,178],"a":[5,31,70,95],"crucial":[6],"area":[7],"of":[8,56,87,101,163],"research":[9],"in":[10,78,106,129,167],"online":[11,157],"advertising.":[12],"While":[13],"binary":[14,32],"cross":[15],"entropy":[16],"(BCE)":[17],"has":[18],"been":[19],"widely":[20],"used":[21],"as":[22,30],"the":[23,53,75,85,99,102,118,123,152],"optimization":[24,119],"objective":[25],"for":[26,90,172],"treating":[27],"CTR":[28,107],"classification":[33,131],"problem,":[34],"recent":[35],"advancements":[36],"have":[37],"shown":[38],"that":[39],"combining":[40],"BCE":[41,76,124],"loss":[42,47,59,77,105,125,154],"with":[43,74],"an":[44],"auxiliary":[45,103],"ranking":[46,104,153],"can":[48],"significantly":[49],"improve":[50],"performance.":[51],"However,":[52],"full":[54],"effectiveness":[55,100],"this":[57,66],"combination":[58],"not":[61],"yet":[62],"fully":[63],"understood.":[64],"In":[65],"paper,":[67],"we":[68,137,149],"uncover":[69],"new":[71],"challenge":[72],"associated":[73],"scenarios":[79],"where":[80],"positive":[81],"feedback":[82],"sparse:":[84],"issue":[86],"gradient":[88],"vanishing":[89],"negative":[91,114],"samples.":[92],"We":[93],"introduce":[94],"novel":[96],"perspective":[97],"on":[98,113,145],"prediction:":[108],"it":[109],"generates":[110],"larger":[111],"gradients":[112],"samples,":[115],"thereby":[116],"mitigating":[117],"difficulties":[120],"when":[121],"using":[122],"only":[126],"and":[127,141,165],"resulting":[128],"improved":[130],"ability.":[132],"To":[133],"validate":[134],"our":[135],"perspective,":[136],"conduct":[138],"theoretical":[139],"analysis":[140],"extensive":[142],"empirical":[143],"evaluations":[144],"public":[146],"datasets.":[147],"Additionally,":[148],"successfully":[150],"integrate":[151],"into":[155],"Tencent's":[156],"advertising":[158],"system,":[159],"achieving":[160],"notable":[161],"lifts":[162],"0.70%":[164],"1.26%":[166],"Gross":[168],"Merchandise":[169],"Value":[170],"(GMV)":[171],"two":[173],"main":[174],"scenarios.":[175],"The":[176],"code":[177],"openly":[179],"accessible":[180],"at:":[181],"https://github.com/SkylerLinn/Understanding-the-Ranking-Loss.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
