{"id":"https://openalex.org/W7162471696","doi":"https://doi.org/10.48550/arxiv.2605.24986","title":"Self-Balancing Gradient Allocation for Heterogeneity-Aware Feature Generation in Click-Through Rate Prediction","display_name":"Self-Balancing Gradient Allocation for Heterogeneity-Aware Feature Generation in Click-Through Rate Prediction","publication_year":2026,"publication_date":"2026-05-24","ids":{"openalex":"https://openalex.org/W7162471696","doi":"https://doi.org/10.48550/arxiv.2605.24986"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24986","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24986","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.24986","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012930762","display_name":"Moyu Zhang","orcid":"https://orcid.org/0000-0002-9104-1881"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Moyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137068699","display_name":"Yun Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109465593","display_name":"Yujun Jin","orcid":"https://orcid.org/0009-0003-0498-5804"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Yujun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137006243","display_name":"Jinxin Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Jinxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137015624","display_name":"Yu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137019514","display_name":"Xiaoyi Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Xiaoyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.460999995470047,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.460999995470047,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.36390000581741333,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.017799999564886093,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.7520999908447266},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7480000257492065},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5929999947547913},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5842999815940857},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5590000152587891},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5335000157356262},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4417000114917755},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4050000011920929}],"concepts":[{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.7520999908447266},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7480000257492065},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6007000207901001},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5929999947547913},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5842999815940857},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5590000152587891},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5532000064849854},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5335000157356262},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4447999894618988},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4417000114917755},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4050000011920929},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4009999930858612},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3666999936103821},{"id":"https://openalex.org/C167879884","wikidata":"https://www.wikidata.org/wiki/Q727568","display_name":"Balanced flow","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31189998984336853},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2879999876022339},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.28110000491142273},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C3019722297","wikidata":"https://www.wikidata.org/wiki/Q4440864","display_name":"High dimensional","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24986","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24986","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.24986","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24986","pdf_url":null,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generative":[0],"pre-training":[1],"via":[2],"discrete":[3],"diffusion":[4],"provides":[5],"dense":[6],"reconstruction":[7,34,49],"supervision":[8],"across":[9,51],"all":[10,24],"feature":[11,42],"fields":[12,66,77,126,144],"simultaneously,":[13],"mitigating":[14],"representation":[15],"collapse":[16],"from":[17],"data":[18],"sparsity":[19],"in":[20],"CTR":[21,27,169],"prediction.":[22],"However,":[23],"existing":[25],"generative":[26,86],"methods":[28],"share":[29,155],"a":[30,81,116,128,133,172],"fundamental":[31],"limitation:":[32],"the":[33,45,72,85,103,139,156],"objective":[35],"assigns":[36],"equal":[37],"training":[38,69],"weight":[39],"to":[40,67],"every":[41],"field,":[43],"ignoring":[44],"profound":[46],"heterogeneity":[47],"of":[48,141],"difficulty":[50,87,98],"high-cardinality":[52],"ID":[53],"fields,":[54],"sparse":[55],"categorical":[56],"attributes,":[57],"numerical":[58],"values,":[59],"and":[60,132,160,171,190],"behavioral":[61],"sequences.":[62],"This":[63,106],"causes":[64],"easy":[65,143],"dominate":[68],"gradients":[70],"while":[71,145],"hardest":[73],"but":[74],"most":[75],"informative":[76],"remain":[78,161],"chronically":[79],"underfit,":[80],"problem":[82],"we":[83],"term":[84],"imbalance.We":[88],"propose":[89],"HeteGenCTR,":[90],"which":[91],"resolves":[92],"this":[93],"imbalance":[94],"through":[95],"per-field":[96],"learnable":[97],"parameters":[99],"jointly":[100],"trained":[101],"with":[102,127,185],"denoising":[104],"network.":[105],"unified":[107],"signal":[108,159],"drives":[109],"two":[110],"coordinated":[111],"components":[112,154],"without":[113],"additional":[114],"hyperparameters:":[115],"self-balancing":[117],"loss":[118],"that":[119,137],"automatically":[120],"reallocates":[121],"gradient":[122],"budget":[123],"toward":[124,150],"harder":[125],"provably":[129],"stable":[130],"equilibrium,":[131],"difficulty-guided":[134],"attention":[135],"mechanism":[136],"suppresses":[138],"influence":[140],"already-converged":[142],"amplifying":[146],"cross-field":[147],"information":[148],"flow":[149],"hard":[151],"fields.":[152],"Both":[153],"same":[157],"learned":[158],"mutually":[162],"consistent":[163],"throughout":[164],"training.":[165],"Experiments":[166],"on":[167],"five":[168],"benchmarks":[170],"seven-day":[173],"online":[174],"A/B":[175],"test":[176],"demonstrate":[177],"consistent,":[178],"statistically":[179],"significant":[180],"improvements":[181],"over":[182],"state-of-the-art":[183],"baselines,":[184],"disproportionate":[186],"gains":[187],"for":[188],"cold-start":[189],"long-tail":[191],"users.":[192]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}