{"id":"https://openalex.org/W4406457793","doi":"https://doi.org/10.1109/bigdata62323.2024.10825602","title":"ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation","display_name":"ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406457793","doi":"https://doi.org/10.1109/bigdata62323.2024.10825602"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825602","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825602","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2604.14114","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111360500","display_name":"Xiaofan Zhou","orcid":"https://orcid.org/0009-0003-5722-1242"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xiaofan Zhou","raw_affiliation_strings":["Worcester Polytechnic Institute,Worcester,USA"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute,Worcester,USA","institution_ids":["https://openalex.org/I107077323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103224637","display_name":"Kyumin Lee","orcid":"https://orcid.org/0000-0002-9004-1740"},"institutions":[{"id":"https://openalex.org/I107077323","display_name":"Worcester Polytechnic Institute","ror":"https://ror.org/05ejpqr48","country_code":"US","type":"education","lineage":["https://openalex.org/I107077323"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kyumin Lee","raw_affiliation_strings":["Worcester Polytechnic Institute,Worcester,USA"],"affiliations":[{"raw_affiliation_string":"Worcester Polytechnic Institute,Worcester,USA","institution_ids":["https://openalex.org/I107077323"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5111360500"],"corresponding_institution_ids":["https://openalex.org/I107077323"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40526611,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"690","last_page":"699"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9947999715805054,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9884999990463257,"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":0.7695321440696716},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4988870620727539},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46805447340011597},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.46193066239356995},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.38777977228164673},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.186280757188797},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.10603031516075134}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7695321440696716},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4988870620727539},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46805447340011597},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.46193066239356995},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38777977228164673},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.186280757188797},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.10603031516075134},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825602","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825602","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2604.14114","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2604.14114","pdf_url":"https://arxiv.org/pdf/2604.14114","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2604.14114","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14114","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2604.14114","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2604.14114","pdf_url":"https://arxiv.org/pdf/2604.14114","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W2140310134","https://openalex.org/W2154908680","https://openalex.org/W2783272285","https://openalex.org/W2798991696","https://openalex.org/W2899457523","https://openalex.org/W2945827670","https://openalex.org/W2963367478","https://openalex.org/W2984100107","https://openalex.org/W2998431760","https://openalex.org/W3034236656","https://openalex.org/W3035524453","https://openalex.org/W3045200674","https://openalex.org/W3100278010","https://openalex.org/W3101707147","https://openalex.org/W3133849783","https://openalex.org/W3195554589","https://openalex.org/W3206127589","https://openalex.org/W3211143493","https://openalex.org/W4221151551","https://openalex.org/W4221155633","https://openalex.org/W4224315226","https://openalex.org/W4224316819","https://openalex.org/W4224326201","https://openalex.org/W4285428788","https://openalex.org/W4293790978","https://openalex.org/W4297808394","https://openalex.org/W4306317239","https://openalex.org/W4320165462","https://openalex.org/W4321485149","https://openalex.org/W4321593910","https://openalex.org/W4324311885","https://openalex.org/W4353115442","https://openalex.org/W4367046636","https://openalex.org/W4367046711","https://openalex.org/W4367046739","https://openalex.org/W4367047145","https://openalex.org/W4367047287","https://openalex.org/W4367047350","https://openalex.org/W4367189289","https://openalex.org/W4372347502","https://openalex.org/W4376122444","https://openalex.org/W4384648968","https://openalex.org/W4384652641","https://openalex.org/W4384655811","https://openalex.org/W4384655952","https://openalex.org/W4384828132","https://openalex.org/W4385562487","https://openalex.org/W4401090713","https://openalex.org/W6688089849","https://openalex.org/W6692935382","https://openalex.org/W6774314701","https://openalex.org/W6800395734","https://openalex.org/W6809730819","https://openalex.org/W6844194202"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Sequential":[0],"recommendation":[1,141],"has":[2,92],"become":[3],"an":[4],"increasingly":[5],"prominent":[6],"subject":[7],"both":[8,151,196],"in":[9,52,127,243,249],"academia":[10],"and":[11,33,59,102,110,153,172,198,201,245,258],"industrial":[12],"sectors,":[13],"particularly":[14],"within":[15],"the":[16,35,39,53,100,125,173,180,184,216,253],"e-commerce":[17],"domain.":[18],"Its":[19],"primary":[20],"aim":[21],"is":[22,118,145],"to":[23,63,104,147,241,247,252],"extract":[24,64],"user":[25,40,109,207],"preference":[26],"from":[27,68,150,183],"a":[28,50,132,189,205,210],"user\u2019s":[29,69],"historical":[30,70],"item":[31,71,111],"list":[32],"predict":[34],"subsequent":[36],"items":[37],"that":[38,45],"might":[41],"purchase":[42,209],"based":[43,83],"on":[44,94,230],"history.":[46],"Recent":[47],"trends":[48],"show":[49],"surge":[51],"application":[54],"of":[55,77,108,160,218,239],"using":[56],"contrastive":[57,96,161,185],"learning":[58,113,138],"graph-based":[60],"neural":[61],"network":[62],"more":[65,86],"expressive":[66],"representation":[67,84,112],"list,":[72],"where":[73],"graph":[74,103,154,170],"contains":[75,85],"information":[76,149],"relationship":[78],"between":[79,99],"nodes":[80],"while":[81],"ID":[82,101],"specific":[87],"information.":[88,122],"However,":[89],"limited":[90],"work":[91],"explored":[93],"multi-view":[95,190],"learning,":[97,186],"especially,":[98],"further":[105],"improve":[106],"quality":[107],"when":[114],"only":[115],"interaction":[116],"data":[117],"available":[119,261],"without":[120],"auxiliary":[121],"To":[123,178],"fill":[124],"gap,":[126],"this":[128],"study,":[129],"we":[130,187],"propose":[131,188],"novel":[133],"framework":[134,144],"called":[135],"MultiView":[136],"Contrastive":[137],"for":[139,164,169,176],"sequential":[140,165],"(MVCrec).":[142],"This":[143],"designed":[146],"combine":[148],"sequential/ID":[152],"views.":[155],"It":[156],"incorporates":[157],"three":[158],"facets":[159],"learning:":[162],"one":[163,168,175],"view,":[166],"another":[167],"view":[171],"other":[174],"cross-view.":[177],"leverage":[179],"representations":[181],"derived":[182],"attention":[191],"fusion":[192],"module,":[193],"which":[194],"integrates":[195],"global":[197],"local":[199],"attentions":[200],"measures":[202],"how":[203],"likely":[204],"target":[206,211],"will":[208],"item.":[212],"Comprehensive":[213],"experiments":[214],"demonstrate":[215],"superiority":[217],"our":[219],"model":[220,236],"over":[221],"11":[222],"state-of-the-art":[223],"baselines,":[224],"as":[225],"evidenced":[226],"by":[227],"its":[228],"performance":[229],"five":[231],"real-world":[232],"benchmark":[233],"datasets.":[234],"Our":[235,256],"achieves":[237],"improvements":[238],"up":[240,246],"14.44%":[242],"NDCG@10":[244],"9.22%":[248],"HitRatio@10":[250],"compared":[251],"best":[254],"baseline.":[255],"code":[257],"datasets":[259],"are":[260],"at":[262],"https://github.com/sword-Lz/MMCrec.":[263]},"counts_by_year":[],"updated_date":"2026-04-18T07:56:08.524223","created_date":"2025-10-10T00:00:00"}
