{"id":"https://openalex.org/W7214289232","doi":"https://doi.org/10.1145/3773078.3831909","title":"HIPA-Net: Heterogeneous Intent Projection Alignment for Feedback Calibration in CVR Prediction","display_name":"HIPA-Net: Heterogeneous Intent Projection Alignment for Feedback Calibration in CVR Prediction","publication_year":2026,"publication_date":"2026-09-25","ids":{"openalex":"https://openalex.org/W7214289232","doi":"https://doi.org/10.1145/3773078.3831909"},"language":"en","primary_location":{"id":"doi:10.1145/3773078.3831909","doi":"https://doi.org/10.1145/3773078.3831909","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773078.3831909","pdf_url":null,"source":{"id":"https://openalex.org/S7407086088","display_name":"ACM Conference on Recommender Systems (RecSys)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 20th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1145/3773078.3831909","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032236566","display_name":"Mengqing Ye","orcid":"https://orcid.org/0000-0002-3382-8432","observed_orcids":["https://orcid.org/0000-0002-3382-8432"]},"institutions":[{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":"CN","type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengqing Ye","raw_affiliation_strings":["Kuaishou Technology, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0000-0002-3382-8432","affiliations":[{"raw_affiliation_string":"Kuaishou Technology, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I4401726859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124804447","display_name":"JianWei Zhai","orcid":"https://orcid.org/0009-0005-7027-9056","observed_orcids":["https://orcid.org/0009-0005-7027-9056"]},"institutions":[{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":"CN","type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianwei Zhai","raw_affiliation_strings":["Kuaishou Technology, Hangzhou, Zhejiang, China"],"raw_orcid":"https://orcid.org/0009-0005-7027-9056","affiliations":[{"raw_affiliation_string":"Kuaishou Technology, Hangzhou, Zhejiang, China","institution_ids":["https://openalex.org/I4401726859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5152929976","display_name":"Bobo Cheng","orcid":"https://orcid.org/0009-0009-3697-1291","observed_orcids":["https://orcid.org/0009-0009-3697-1291"]},"institutions":[{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":"CN","type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bobo Cheng","raw_affiliation_strings":["Kuaishou Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0009-3697-1291","affiliations":[{"raw_affiliation_string":"Kuaishou Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100646705","display_name":"Fei Pan","orcid":"https://orcid.org/0009-0007-1739-0868","observed_orcids":["https://orcid.org/0009-0007-1739-0868","https://orcid.org/0009-0001-1771-0397"]},"institutions":[{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":"CN","type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Pan","raw_affiliation_strings":["Kuaishou Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-1739-0868","affiliations":[{"raw_affiliation_string":"Kuaishou Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001339397","display_name":"Peng Jiang","orcid":"https://orcid.org/0000-0002-9266-0780","observed_orcids":["https://orcid.org/0000-0002-9266-0780","https://orcid.org/0009-0000-8687-4056","https://orcid.org/0000-0001-8133-5010"]},"institutions":[{"id":"https://openalex.org/I4401726859","display_name":"Kuaishou (China)","ror":"https://ror.org/0258as409","country_code":"CN","type":"company","lineage":["https://openalex.org/I4401726859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Jiang","raw_affiliation_strings":["Kuaishou Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9266-0780","affiliations":[{"raw_affiliation_string":"Kuaishou Technology, Beijing, China","institution_ids":["https://openalex.org/I4401726859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4401726859"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72342839,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1167","last_page":"1176"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9984999895095825,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9984999895095825,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.000699999975040555,"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/T10609","display_name":"Digital Marketing and Social Media","score":9.999999747378752e-05,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/negative-feedback","display_name":"negative feedback","score":0.41600000858306885}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7763000130653381},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.6679999828338623},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6492000222206116},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.6319000124931335},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5343999862670898},{"id":"https://openalex.org/C93586867","wikidata":"https://www.wikidata.org/wiki/Q62527","display_name":"Negative feedback","level":3,"score":0.4410000145435333},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.43560001254081726},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4083000123500824},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.38499999046325684},{"id":"https://openalex.org/C186886427","wikidata":"https://www.wikidata.org/wiki/Q5441213","display_name":"Feedback loop","level":2,"score":0.36039999127388},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3449000120162964},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34040001034736633},{"id":"https://openalex.org/C2780102126","wikidata":"https://www.wikidata.org/wiki/Q10928179","display_name":"Online and offline","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C59656382","wikidata":"https://www.wikidata.org/wiki/Q191536","display_name":"Conjunction (astronomy)","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31940001249313354},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2976999878883362},{"id":"https://openalex.org/C2779532271","wikidata":"https://www.wikidata.org/wiki/Q445558","display_name":"Relevance feedback","level":4,"score":0.29179999232292175},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.26409998536109924},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C2777851325","wikidata":"https://www.wikidata.org/wiki/Q7094102","display_name":"Online model","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3773078.3831909","doi":"https://doi.org/10.1145/3773078.3831909","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773078.3831909","pdf_url":null,"source":{"id":"https://openalex.org/S7407086088","display_name":"ACM Conference on Recommender Systems (RecSys)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 20th ACM Conference on Recommender Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3773078.3831909","doi":"https://doi.org/10.1145/3773078.3831909","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773078.3831909","pdf_url":null,"source":{"id":"https://openalex.org/S7407086088","display_name":"ACM Conference on Recommender Systems (RecSys)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"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 20th ACM Conference on Recommender Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W2021866613","https://openalex.org/W2074694452","https://openalex.org/W2076618162","https://openalex.org/W2101409192","https://openalex.org/W2475334473","https://openalex.org/W2512971201","https://openalex.org/W2517388038","https://openalex.org/W2604662567","https://openalex.org/W2723293840","https://openalex.org/W2793768763","https://openalex.org/W2893359107","https://openalex.org/W2945772520","https://openalex.org/W2950694221","https://openalex.org/W2962745591","https://openalex.org/W2962989965","https://openalex.org/W2964182926","https://openalex.org/W2982902390","https://openalex.org/W2994850640","https://openalex.org/W3033630125","https://openalex.org/W3035287707","https://openalex.org/W3035313290","https://openalex.org/W3035596828","https://openalex.org/W3093519337","https://openalex.org/W3096591391","https://openalex.org/W3128744564","https://openalex.org/W3187615801","https://openalex.org/W3207257408","https://openalex.org/W4223553144","https://openalex.org/W4223591050","https://openalex.org/W4226337336","https://openalex.org/W4280650645","https://openalex.org/W4285428788","https://openalex.org/W4285600332","https://openalex.org/W4297435963","https://openalex.org/W4361230837","https://openalex.org/W4385562613","https://openalex.org/W4396735047","https://openalex.org/W4403220171","https://openalex.org/W4403582478","https://openalex.org/W4409657433","https://openalex.org/W4414034827","https://openalex.org/W4414034912"],"related_works":["https://openalex.org/W4402817483","https://openalex.org/W7126069073","https://openalex.org/W4376163514","https://openalex.org/W4414034869","https://openalex.org/W4409233917","https://openalex.org/W4299296448","https://openalex.org/W7167912077","https://openalex.org/W7081282844","https://openalex.org/W4405306095","https://openalex.org/W4293767273"],"abstract_inverted_index":{"Lead":[0],"advertising":[1,135],"is":[2,15,33,46],"a":[3,74,89,131],"contact-oriented":[4,174],"conversion":[5],"scenario":[6],"in":[7,29,130],"industrial":[8,133],"recommender":[9],"systems,":[10],"where":[11],"the":[12,167],"target":[13],"action":[14],"to":[16],"initiate":[17],"follow-up":[18],"communication":[19],"rather":[20],"than":[21],"complete":[22],"an":[23],"immediate":[24],"transaction.":[25],"A":[26],"key":[27],"challenge":[28],"lead":[30,70,134],"CVR":[31,71],"prediction":[32,72],"that":[34,140],"implicit":[35,61,119],"drop-off":[36,52,104,120],"behaviors":[37],"are":[38],"frequent":[39],"and":[40,103,110,117,148,160],"informative,":[41],"but":[42],"their":[43],"feedback":[44,57,62,76,105,164],"polarity":[45],"not":[47],"directly":[48],"observed.":[49],"Treating":[50],"such":[51],"evidence":[53,121],"as":[54,73],"fixed":[55],"negative":[56,111,163],"or":[58],"ordinary":[59],"noisy":[60],"can":[63],"distort":[64],"user":[65],"contact":[66,81],"intent.":[67],"We":[68],"formulate":[69],"semantic":[75,171],"calibration":[77,172],"problem":[78],"for":[79,99,173],"unfinished":[80],"behaviors.":[82],"To":[83],"address":[84],"it,":[85],"we":[86],"propose":[87],"HIPA-Net,":[88],"Heterogeneous":[90],"Intent":[91],"Projection":[92],"Alignment":[93],"Network.":[94],"HIPA-Net":[95,126,141],"learns":[96],"candidate-aware":[97],"representations":[98],"fine-grained":[100],"positive,":[101],"negative,":[102],"behaviors,":[106],"constructs":[107],"explicit":[108],"positive":[109],"intent":[112],"anchors":[113],"from":[114],"reliable":[115],"feedback,":[116],"calibrates":[118],"through":[122],"anchor-specific":[123],"projection":[124],"alignment.":[125],"has":[127],"been":[128],"deployed":[129],"large-scale":[132],"system.":[136],"Offline":[137],"experiments":[138],"show":[139,155],"outperforms":[142],"representative":[143],"behavior":[144],"modeling,":[145],"multi-behavior,":[146],"feedback-aware,":[147],"denoising":[149],"baselines.":[150],"Online":[151],"A/B":[152],"tests":[153],"further":[154],"+2.184%":[156],"CTR,":[157],"+5.426%":[158],"RPM,":[159],"-11.521%":[161],"user-side":[162],"rate,":[165],"demonstrating":[166],"effectiveness":[168],"of":[169],"anchor-guided":[170],"recommendation.":[175]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-09-26T00:00:00"}
