{"id":"https://openalex.org/W4324266429","doi":"https://doi.org/10.1145/3579654.3579775","title":"Research and application of matching network","display_name":"Research and application of matching network","publication_year":2022,"publication_date":"2022-12-23","ids":{"openalex":"https://openalex.org/W4324266429","doi":"https://doi.org/10.1145/3579654.3579775"},"language":"en","primary_location":{"id":"doi:10.1145/3579654.3579775","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3579654.3579775","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067502052","display_name":"Chao Jiang","orcid":"https://orcid.org/0000-0002-7123-6702"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Jiang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0002-7123-6702","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074855137","display_name":"Junyang Mo","orcid":"https://orcid.org/0000-0002-2981-9349"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junyang Mo","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0003-0420-9912","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060907745","display_name":"Zhongming Pan","orcid":"https://orcid.org/0000-0003-0143-7389"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhongming Pan","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, China"],"raw_orcid":"https://orcid.org/0000-0003-0143-7389","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139759216"],"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":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9945999979972839,"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/T10028","display_name":"Topic Modeling","score":0.9945999979972839,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.9732000231742859,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9700999855995178,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8347634077072144},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.7355204820632935},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.714241623878479},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6813334822654724},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.601071834564209},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5712705850601196},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5225619673728943},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5213378667831421},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4394074082374573},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4273267090320587},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.37700676918029785}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8347634077072144},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.7355204820632935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.714241623878479},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6813334822654724},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.601071834564209},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5712705850601196},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5225619673728943},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5213378667831421},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4394074082374573},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4273267090320587},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.37700676918029785},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3579654.3579775","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3579654.3579775","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.8500000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2100495367","https://openalex.org/W2892181857","https://openalex.org/W2897046312","https://openalex.org/W2947681066","https://openalex.org/W2952370363","https://openalex.org/W2996428491","https://openalex.org/W2997200074","https://openalex.org/W3100258764","https://openalex.org/W3156636935","https://openalex.org/W3176015924","https://openalex.org/W3209059054","https://openalex.org/W4287824654"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2502115930","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W1989889224","https://openalex.org/W4382618745","https://openalex.org/W1973775000","https://openalex.org/W2748922771","https://openalex.org/W1987128138"],"abstract_inverted_index":{"With":[0],"the":[1,38,48,59,65,83,89,109,112,119,122,129,138],"rapid":[2],"development":[3],"of":[4,24,32,64,68,121,131,140],"deep":[5,17,33],"learning":[6,18,34,100],"and":[7,12,55,70,96,101,134],"natural":[8],"language":[9],"processing,":[10],"more":[11,13],"systems":[14],"have":[15,58],"applied":[16],"models.":[19],"However,":[20],"a":[21,29,77],"large":[22],"number":[23],"data":[25,69,95],"for":[26],"training":[27,79,116],"is":[28],"major":[30],"bottleneck":[31],"at":[35],"present.":[36],"For":[37],"postgraduate":[39],"thesis":[40],"oral":[41],"defense":[42],"system,":[43,133],"our":[44,126,132,141],"model":[45,91],"still":[46],"utilizes":[47,98],"word":[49],"retrieval":[50],"method":[51],"to":[52,81,104,117],"match":[53],"teachers":[54],"students":[56],"who":[57],"same":[60,110],"research":[61],"field":[62],"because":[63],"small":[66],"amount":[67],"information.":[71],"In":[72],"this":[73],"paper,":[74],"we":[75],"propose":[76],"two-stage":[78],"framework":[80,113],"improve":[82,118],"system":[84],"matching":[85,102],"correlation":[86],"which":[87],"fine-tunes":[88],"pre-trained":[90],"on":[92,128],"specific":[93],"downstream":[94],"then":[97],"contrastive":[99],"network":[103],"conduct":[105],"self-supervised":[106],"training.":[107],"At":[108],"time,":[111],"uses":[114],"adversarial":[115],"robustness":[120],"model.":[123],"We":[124],"evaluate":[125],"approach":[127],"dataset":[130],"experiment":[135],"results":[136],"demonstrate":[137],"effectiveness":[139],"approach.":[142]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
