{"id":"https://openalex.org/W4412877076","doi":"https://doi.org/10.1145/3711896.3737265","title":"Synergizing Implicit and Explicit User Interests: A Multi-Embedding Retrieval Framework at Pinterest","display_name":"Synergizing Implicit and Explicit User Interests: A Multi-Embedding Retrieval Framework at Pinterest","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4412877076","doi":"https://doi.org/10.1145/3711896.3737265"},"language":"en","primary_location":{"id":"doi:10.1145/3711896.3737265","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737265","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737265","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737265","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zhibo Fan","orcid":"https://orcid.org/0009-0001-4014-6245"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhibo Fan","raw_affiliation_strings":["Pinterest, San Francisco, CA, USA"],"raw_orcid":"https://orcid.org/0009-0001-4014-6245","affiliations":[{"raw_affiliation_string":"Pinterest, San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087377507","display_name":"H. H. Lin","orcid":"https://orcid.org/0009-0001-5929-1936"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongtao Lin","raw_affiliation_strings":["Pinterest, San Francisco, CA, USA"],"raw_orcid":"https://orcid.org/0009-0001-5929-1936","affiliations":[{"raw_affiliation_string":"Pinterest, San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100763978","display_name":"Haoyu Chen","orcid":"https://orcid.org/0009-0007-2608-6382"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haoyu Chen","raw_affiliation_strings":["Pinterest, San Francisco, CA, USA"],"raw_orcid":"https://orcid.org/0009-0007-2608-6382","affiliations":[{"raw_affiliation_string":"Pinterest, San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bowen Deng","orcid":"https://orcid.org/0009-0008-6473-8799"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bowen Deng","raw_affiliation_strings":["Pinterest, San Francisco, CA, USA"],"raw_orcid":"https://orcid.org/0009-0008-6473-8799","affiliations":[{"raw_affiliation_string":"Pinterest, San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102139875","display_name":"Henian Xia","orcid":"https://orcid.org/0000-0003-0370-4890"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hedi Xia","raw_affiliation_strings":["Pinterest, San Francisco, CA, USA"],"raw_orcid":"https://orcid.org/0000-0003-0370-4890","affiliations":[{"raw_affiliation_string":"Pinterest, San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuke Yan","orcid":"https://orcid.org/0009-0001-7183-5887"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuke Yan","raw_affiliation_strings":["Pinterest, San Francisco, CA, USA"],"raw_orcid":"https://orcid.org/0009-0001-7183-5887","affiliations":[{"raw_affiliation_string":"Pinterest, San Francisco, CA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007260088","display_name":"James Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"James Li","raw_affiliation_strings":["Pinterest, San Francisco, CA, USA"],"raw_orcid":"https://orcid.org/0009-0004-8492-2877","affiliations":[{"raw_affiliation_string":"Pinterest, San Francisco, CA, USA","institution_ids":[]}]}],"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":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4396","last_page":"4405"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9983999729156494,"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/T10028","display_name":"Topic Modeling","score":0.9950000047683716,"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/computer-science","display_name":"Computer science","score":0.750449538230896},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.7257238030433655},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.5770131349563599},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.36646419763565063},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.22561749815940857}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.750449538230896},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7257238030433655},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.5770131349563599},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.36646419763565063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22561749815940857}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3711896.3737265","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737265","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737265","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2506.23060","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.23060","pdf_url":"https://arxiv.org/pdf/2506.23060","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:10.1145/3711896.3737265","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3711896.3737265","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3711896.3737265","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412877076.pdf","grobid_xml":"https://content.openalex.org/works/W4412877076.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W2100142570","https://openalex.org/W2186845332","https://openalex.org/W2512971201","https://openalex.org/W2593507512","https://openalex.org/W2807021761","https://openalex.org/W2963265099","https://openalex.org/W2963367478","https://openalex.org/W2963469388","https://openalex.org/W2964694324","https://openalex.org/W2966123616","https://openalex.org/W2972801466","https://openalex.org/W2982902390","https://openalex.org/W3044311607","https://openalex.org/W3080642298","https://openalex.org/W3100324210","https://openalex.org/W3100848837","https://openalex.org/W3116048950","https://openalex.org/W3171608958","https://openalex.org/W4284673697","https://openalex.org/W4290927925","https://openalex.org/W4385270317","https://openalex.org/W4385568029","https://openalex.org/W4401857632","https://openalex.org/W4403221410"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2081900870","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890"],"abstract_inverted_index":{"Industrial":[0],"recommendation":[1],"systems":[2],"are":[3,105,127],"typically":[4],"composed":[5],"of":[6,26,34,138],"multiple":[7,92],"stages,":[8],"including":[9],"retrieval,":[10],"ranking,":[11],"and":[12,42,66,99,148,159,172,183,193,202],"blending.":[13],"The":[14],"retrieval":[15,82,175],"stage":[16,48],"plays":[17],"a":[18,23,31,50,79,111,136,165],"critical":[19],"role":[20],"in":[21,57,199],"generating":[22,91],"high-recall":[24],"set":[25],"candidate":[27,174],"items":[28],"that":[29,122,143],"covers":[30],"wide":[32],"range":[33],"diverse":[35,41],"user":[36,44,87,93,101,108,124,140,161,181,200],"interests.":[37,102],"Effectively":[38],"covering":[39],"the":[40,123,150,154],"long-tail":[43],"interests":[45,104,162],"within":[46],"this":[47,58],"poses":[49],"significant":[51,197],"challenge:":[52],"traditional":[53],"two-tower":[54],"models":[55],"struggle":[56],"regard":[59],"due":[60],"to":[61,85,168],"limited":[62],"user-item":[63],"feature":[64],"interaction":[65],"often":[67],"bias":[68],"towards":[69],"top":[70],"use":[71],"cases.":[72],"To":[73],"address":[74],"these":[75],"issues,":[76],"we":[77],"propose":[78],"novel":[80],"multi-embedding":[81],"framework":[83,208],"designed":[84],"enhance":[86],"interest":[88],"representation":[89,141,146],"by":[90],"embeddings":[94],"conditioned":[95,139],"on":[96,179,213],"both":[97],"implicit":[98,158],"explicit":[100,117,160],"Implicit":[103],"captured":[106],"from":[107,186],"history":[109],"through":[110],"Differentiable":[112],"Clustering":[113],"Module":[114],"(DCM),":[115],"whereas":[116],"interests,":[118],"such":[119],"as":[120,164,176],"topics":[121],"has":[125,209],"followed,":[126],"modeled":[128],"via":[129],"Conditional":[130],"Retrieval":[131],"(CR).":[132],"These":[133],"methodologies":[134],"represent":[135],"form":[137],"learning":[142],"involves":[144],"condition":[145],"construction":[147],"associating":[149],"target":[151],"item":[152],"with":[153],"relevant":[155],"conditions.":[156],"Synergizing":[157],"serves":[163],"complementary":[166],"approach":[167],"achieve":[169],"more":[170],"effective":[171],"comprehensive":[173],"they":[177],"benefit":[178],"different":[180,187],"segments":[182],"extract":[184],"conditions":[185],"but":[188],"supplementary":[189],"sources.":[190],"Extensive":[191],"experiments":[192],"A/B":[194],"testing":[195],"reveal":[196],"improvements":[198],"engagements":[201],"feed":[203],"diversity":[204],"metrics.":[205],"Our":[206],"proposed":[207],"been":[210],"successfully":[211],"deployed":[212],"Pinterest":[214],"home":[215],"feed.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-07-16T13:24:37.021932","created_date":"2025-10-10T00:00:00"}