{"id":"https://openalex.org/W4402572184","doi":"https://doi.org/10.1109/snpd61259.2024.10673952","title":"Coupling Semantic Association Graphs with Contrastive Learning in Recommendation","display_name":"Coupling Semantic Association Graphs with Contrastive Learning in Recommendation","publication_year":2024,"publication_date":"2024-07-05","ids":{"openalex":"https://openalex.org/W4402572184","doi":"https://doi.org/10.1109/snpd61259.2024.10673952"},"language":"en","primary_location":{"id":"doi:10.1109/snpd61259.2024.10673952","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/snpd61259.2024.10673952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/ACIS 27th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","raw_type":"proceedings-article"},"type":"article","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/A5102497859","display_name":"Yifan Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I75689368","display_name":"Communication University of China","ror":"https://ror.org/04facbs33","country_code":"CN","type":"education","lineage":["https://openalex.org/I75689368"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yifan Ji","raw_affiliation_strings":["University of China,School of Computer and Cyber Sciences Communication,Beijing,China"],"affiliations":[{"raw_affiliation_string":"University of China,School of Computer and Cyber Sciences Communication,Beijing,China","institution_ids":["https://openalex.org/I75689368"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101850837","display_name":"Jing Zhou","orcid":"https://orcid.org/0000-0002-4294-3985"},"institutions":[{"id":"https://openalex.org/I75689368","display_name":"Communication University of China","ror":"https://ror.org/04facbs33","country_code":"CN","type":"education","lineage":["https://openalex.org/I75689368"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Zhou","raw_affiliation_strings":["University of China,School of Computer and Cyber Sciences Communication,Beijing,China"],"affiliations":[{"raw_affiliation_string":"University of China,School of Computer and Cyber Sciences Communication,Beijing,China","institution_ids":["https://openalex.org/I75689368"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102497859"],"corresponding_institution_ids":["https://openalex.org/I75689368"],"apc_list":null,"apc_paid":null,"fwci":0.8118,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79196808,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"234","last_page":"239"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9955000281333923,"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":0.9955000281333923,"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/T10028","display_name":"Topic Modeling","score":0.9876999855041504,"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/T12488","display_name":"Mental Health via Writing","score":0.9638000130653381,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.7017731666564941},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6321930289268494},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5153648257255554},{"id":"https://openalex.org/keywords/coupling","display_name":"Coupling (piping)","score":0.486989825963974},{"id":"https://openalex.org/keywords/association-rule-learning","display_name":"Association rule learning","score":0.4850148856639862},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48268842697143555},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.24403774738311768},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.17083141207695007}],"concepts":[{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.7017731666564941},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6321930289268494},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5153648257255554},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.486989825963974},{"id":"https://openalex.org/C193524817","wikidata":"https://www.wikidata.org/wiki/Q386780","display_name":"Association rule learning","level":2,"score":0.4850148856639862},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48268842697143555},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.24403774738311768},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.17083141207695007},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/snpd61259.2024.10673952","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/snpd61259.2024.10673952","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/ACIS 27th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.4300000071525574}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2010187764","https://openalex.org/W2140310134","https://openalex.org/W2153007951","https://openalex.org/W2509893387","https://openalex.org/W2792839191","https://openalex.org/W2884134047","https://openalex.org/W2913560138","https://openalex.org/W2945623882","https://openalex.org/W2963707260","https://openalex.org/W3031918285","https://openalex.org/W3045200674","https://openalex.org/W3094605801","https://openalex.org/W3129482887","https://openalex.org/W3172710079","https://openalex.org/W3208338073","https://openalex.org/W4220909642","https://openalex.org/W4284666445"],"related_works":["https://openalex.org/W2751920613","https://openalex.org/W2415164632","https://openalex.org/W2238349241","https://openalex.org/W2355668701","https://openalex.org/W2370453500","https://openalex.org/W1561334777","https://openalex.org/W3012205960","https://openalex.org/W2392697706","https://openalex.org/W366033468","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Knowledge":[0],"graphs":[1],"(KGs)":[2],"are":[3],"typically":[4],"used":[5],"to":[6,17,63,93,142],"enhance":[7],"the":[8,19,25,57,81,85,160,164,175,185],"performance":[9,58,186],"of":[10,59,96,153,177,187],"recommender":[11,42,60,108,188],"systems":[12,109],"by":[13],"leveraging":[14],"their":[15],"characteristic":[16],"supplement":[18],"sparse":[20],"user-item":[21],"interaction":[22],"data":[23],"in":[24,41,55,68,84,183],"latter.":[26],"This":[27],"is":[28],"because":[29],"KGs":[30],"feature":[31,119],"abundant":[32],"semantic":[33,98,135,149],"information":[34],"about":[35],"entities":[36],"(e.g.":[37],"users":[38],"or":[39,71],"items":[40],"systems)":[43],"and":[44,146,159],"inter-entity":[45],"relationships.":[46],"Deep":[47],"learning":[48,89,114,182],"has":[49],"been":[50,156],"seen":[51],"gain":[52],"wide":[53],"use":[54],"boosting":[56],"systems.":[61,189],"Thanks":[62],"focusing":[64],"merely":[65],"on":[66,171],"nodes":[67],"a":[69,110],"KG,":[70],"entities,":[72],"such":[73],"an":[74,133],"approach,":[75],"however,":[76],"suffers":[77],"from":[78,121],"well":[79],"capturing":[80],"semantics":[82],"hidden":[83],"node":[86,90],"connectivity":[87],"when":[88],"embeddings,":[91,130],"leading":[92],"undesirable":[94],"discovery":[95],"potential":[97],"associations":[99],"between":[100],"nodes.":[101],"Against":[102],"this":[103],"background,":[104],"we":[105,131],"propose":[106],"for":[107],"multi-level":[111],"cross-view":[112],"contrastive":[113,181],"mechanism":[115,166],"that":[116,163],"learns":[117],"high-quality":[118],"representations":[120],"unlabeled":[122],"data.":[123],"By":[124],"incorporating":[125],"item-item":[126],"correlations":[127],"into":[128],"item":[129],"developed":[132],"interitem":[134],"association":[136],"graph":[137],"(SAG),":[138],"which":[139],"was":[140],"intended":[141],"model":[143],"more":[144],"comprehensive":[145],"finer-grained":[147],"inter-item":[148],"associations.":[150],"A":[151],"series":[152],"experiments":[154],"have":[155],"carried":[157],"out":[158],"findings":[161],"verify":[162],"proposed":[165],"outperforms":[167],"other":[168],"baseline":[169],"models":[170],"all":[172],"metrics,":[173],"confirming":[174],"effectiveness":[176],"coupling":[178],"SAGs":[179],"with":[180],"enhancing":[184]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}