{"id":"https://openalex.org/W7130706945","doi":"https://doi.org/10.48550/arxiv.2602.17654","title":"Mine and Refine: Optimizing Graded Relevance in E-commerce Search Retrieval","display_name":"Mine and Refine: Optimizing Graded Relevance in E-commerce Search Retrieval","publication_year":2026,"publication_date":"2026-02-19","ids":{"openalex":"https://openalex.org/W7130706945","doi":"https://doi.org/10.48550/arxiv.2602.17654"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.17654","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126465648","display_name":"Jiaqi Xi","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xi, Jiaqi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094046373","display_name":"Raghav Saboo","orcid":"https://orcid.org/0000-0001-8175-360X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saboo, Raghav","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126467848","display_name":"Luming Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Luming","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126515710","display_name":"Martin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Martin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5028089637","display_name":"Sudeep Das","orcid":"https://orcid.org/0000-0002-1754-5811"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Das, Sudeep","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5126465648"],"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.6633999943733215,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.6633999943733215,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.0820000022649765,"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":0.04270000010728836,"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/relevance","display_name":"Relevance (law)","score":0.7179999947547913},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6377999782562256},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6262000203132629},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5440999865531921},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.3921000063419342},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.36579999327659607},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.35260000824928284},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.3370000123977661}],"concepts":[{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.7179999947547913},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7062000036239624},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6377999782562256},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6262000203132629},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5440999865531921},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.49230000376701355},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.448199987411499},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3921000063419342},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.35260000824928284},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3325999975204468},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.33250001072883606},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33180001378059387},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.2888000011444092},{"id":"https://openalex.org/C2779532271","wikidata":"https://www.wikidata.org/wiki/Q445558","display_name":"Relevance feedback","level":4,"score":0.28369998931884766},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.2567000091075897},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.25049999356269836}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.17654","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},{"id":"doi:10.48550/arxiv.2602.17654","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.17654","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.17654","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"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":{"We":[0],"propose":[1],"a":[2,89,96,114,120,128,152],"two-stage":[3],"\"Mine":[4],"and":[5,41,61,79,100,142,150,169,182,189,200,207],"Refine\"":[6],"contrastive":[7,124],"training":[8],"framework":[9,196],"for":[10,75],"semantic":[11,131],"text":[12],"embeddings":[13,25],"to":[14,28,35,166],"enhance":[15],"multi-category":[16],"e-commerce":[17,22],"search":[18,23],"retrieval.":[19],"Large":[20],"scale":[21],"demands":[24],"that":[26,48,126,158,194],"generalize":[27],"long":[29],"tail,":[30],"noisy":[31],"queries":[32],"while":[33],"adhering":[34],"scalable":[36,83],"supervision":[37],"compatible":[38],"with":[39,119,145],"product":[40],"policy":[42,84,147],"constraints.":[43],"A":[44],"practical":[45],"challenge":[46],"is":[47,50,175],"relevance":[49,73,98,164,199],"often":[51],"graded:":[52],"users":[53],"accept":[54],"substitutes":[55],"or":[56],"complements":[57],"beyond":[58],"exact":[59],"matches,":[60],"production":[62,190],"systems":[63],"benefit":[64],"from":[65],"clear":[66],"separation":[67],"of":[68,155],"similarity":[69,161],"scores":[70],"across":[71],"these":[72],"strata":[74],"stable":[76],"hybrid":[77],"blending":[78],"thresholding.":[80],"To":[81],"obtain":[82],"consistent":[85],"supervision,":[86],"we":[87,112,136],"fine-tune":[88],"lightweight":[90],"LLM":[91],"on":[92],"human":[93],"annotations":[94],"under":[95],"three-level":[97],"guideline":[99],"further":[101,167],"reduce":[102],"residual":[103],"noise":[104],"via":[105,140],"engagement":[106,206],"driven":[107],"auditing.":[108],"In":[109,133],"Stage":[110,134],"1,":[111],"train":[113],"multilingual":[115],"Siamese":[116],"two-tower":[117],"retriever":[118],"label":[121],"aware":[122],"supervised":[123],"objective":[125],"shapes":[127],"robust":[129],"global":[130],"space.":[132,173],"2,":[135],"mine":[137],"hard":[138],"samples":[139],"ANN":[141],"re-annotate":[143],"them":[144],"the":[146,171],"aligned":[148],"LLM,":[149],"introduce":[151],"multi-class":[153],"extension":[154],"circle":[156],"loss":[157],"explicitly":[159],"sharpens":[160],"boundaries":[162],"between":[163],"levels,":[165],"refine":[168],"enrich":[170],"embedding":[172],"Robustness":[174],"additionally":[176],"improved":[177],"through":[178],"additive":[179],"spelling":[180],"augmentation":[181],"synthetic":[183],"query":[184],"generation.":[185],"Extensive":[186],"offline":[187],"evaluations":[188],"A/B":[191],"tests":[192],"show":[193],"our":[195],"improves":[197],"retrieval":[198],"delivers":[201],"statistically":[202],"significant":[203],"gains":[204],"in":[205],"business":[208],"impact.":[209]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-21T00:00:00"}