{"id":"https://openalex.org/W7127650008","doi":"https://doi.org/10.1109/tmm.2026.3654432","title":"Breaking the Curse of Knowledge: Towards Effective Multimodal Recommendation Using Knowledge Soft Integration","display_name":"Breaking the Curse of Knowledge: Towards Effective Multimodal Recommendation Using Knowledge Soft Integration","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7127650008","doi":"https://doi.org/10.1109/tmm.2026.3654432"},"language":null,"primary_location":{"id":"doi:10.1109/tmm.2026.3654432","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2026.3654432","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-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/A5124984671","display_name":"Kai Ouyang","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Ouyang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101955396","display_name":"Chen Tang","orcid":"https://orcid.org/0000-0002-0108-6729"},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"HK","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Chen Tang","raw_affiliation_strings":["MMLab, The Chinese University of Hong Kong, Hong Kong"],"raw_orcid":"https://orcid.org/0000-0002-0108-6729","affiliations":[{"raw_affiliation_string":"MMLab, The Chinese University of Hong Kong, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003827815","display_name":"Zenghao Chai","orcid":"https://orcid.org/0000-0003-3709-4947"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zenghao Chai","raw_affiliation_strings":["National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0003-3709-4947","affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101923966","display_name":"Wenhao Zheng","orcid":"https://orcid.org/0000-0002-7108-370X"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenhao Zheng","raw_affiliation_strings":["Microsoft AI Asia, Suzhou, Jiangsu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft AI Asia, Suzhou, Jiangsu, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113213744","display_name":"Xiangjin Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangjin Xie","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124971863","display_name":"Xuanji Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuanji Xiao","raw_affiliation_strings":["Tsinghua University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5125015327","display_name":"Zhi Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhi Wang","raw_affiliation_strings":["Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5462-6178","affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11127575,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"4525","last_page":"4534"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.8718000054359436,"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":0.8718000054359436,"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":0.031700000166893005,"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":0.011500000022351742,"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/redundancy","display_name":"Redundancy (engineering)","score":0.605400025844574},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.49790000915527344},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.43790000677108765},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42419999837875366},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.41769999265670776},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.41670000553131104},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.3937999904155731},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.38839998841285706},{"id":"https://openalex.org/keywords/optimal-distinctiveness-theory","display_name":"Optimal distinctiveness theory","score":0.37299999594688416}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8521999716758728},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.605400025844574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5234000086784363},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5001000165939331},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.49790000915527344},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.43790000677108765},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4339999854564667},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42419999837875366},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.41769999265670776},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41670000553131104},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3937999904155731},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C47385372","wikidata":"https://www.wikidata.org/wiki/Q7098943","display_name":"Optimal distinctiveness theory","level":2,"score":0.37299999594688416},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.31310001015663147},{"id":"https://openalex.org/C140073362","wikidata":"https://www.wikidata.org/wiki/Q738759","display_name":"Soft computing","level":3,"score":0.30559998750686646},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C85407183","wikidata":"https://www.wikidata.org/wiki/Q1045785","display_name":"Semantic network","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2856000065803528},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26170000433921814},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2026.3654432","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2026.3654432","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4264659296","display_name":null,"funder_award_id":"92467204","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8973540564","display_name":null,"funder_award_id":"62472249","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"A":[0],"critical":[1],"challenge":[2],"in":[3,8,39,68,163,225],"contemporary":[4],"recommendation":[5,15,40,54,227],"systems":[6],"lies":[7],"effectively":[9],"leveraging":[10],"multimodal":[11,43,101,181,226],"content":[12],"to":[13,25,78,96,149,160,178,215],"enhance":[14],"personalization.":[16],"Although":[17],"various":[18],"solutions":[19],"have":[20],"been":[21],"proposed,":[22],"most":[23],"fail":[24],"account":[26],"for":[27],"discrepancies":[28],"between":[29],"knowledge":[30,91,224],"extracted":[31],"through":[32],"isolated":[33],"feature":[34,44],"extraction":[35,45],"and":[36,71,132,165,212,219],"its":[37],"application":[38],"tasks.":[41],"Specifically,":[42],"does":[46],"not":[47],"incorporate":[48],"task-specific":[49],"prior":[50],"knowledge,":[51,183],"while":[52,155],"downstream":[53],"tasks":[55],"typically":[56],"use":[57],"these":[58,204],"features":[59,102],"as":[60,79],"auxiliary":[61],"information.":[62],"This":[63],"misalignment":[64],"often":[65],"introduces":[66],"biases":[67,105],"model":[69,150],"fitting":[70],"degrades":[72],"performance,":[73],"a":[74,90,143,157,174],"phenomenon":[75],"we":[76,88],"refer":[77],"the":[80,98,104,126,133,170,186,207,210,221],"curse":[81,222],"of":[82,100,189,209,223],"knowledge.":[83],"To":[84],"address":[85],"this":[86],"challenge,":[87],"propose":[89],"soft":[92],"integration":[93],"framework":[94],"designed":[95],"balance":[97],"utilization":[99],"with":[103],"they":[106],"may":[107],"introduce.":[108],"The":[109,139],"framework,":[110],"named":[111],"<bold":[112,115,118],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[113,116,119],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">K</b>nowledge":[114],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">S</b>oft":[117],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">I</b>ntegration":[120],"(KSI),":[121],"comprises":[122],"two":[123],"key":[124],"components:":[125],"Structure":[127],"Efficient":[128],"Injection":[129],"(SEI)":[130],"module":[131,141,172],"Semantic":[134],"Soft":[135],"Integration":[136],"(SSI)":[137],"module.":[138],"SEI":[140,211],"employs":[142],"Refined":[144],"Graph":[145],"Neural":[146],"Network":[147],"(RGNN)":[148],"inter-modal":[151],"correlations":[152],"among":[153],"items":[154],"introducing":[156],"regularization":[158],"term":[159],"minimize":[161],"redundancy":[162,218],"user":[164],"item":[166,190],"representations.":[167,191],"In":[168],"parallel,":[169],"SSI":[171,213],"utilizes":[173],"self-supervised":[175],"retrieval":[176],"task":[177],"implicitly":[179],"integrate":[180],"semantic":[182,187],"thereby":[184],"enhancing":[185],"distinctiveness":[188],"We":[192],"conduct":[193],"comprehensive":[194],"experiments":[195],"on":[196],"three":[197],"benchmark":[198],"datasets,":[199],"demonstrating":[200],"KSI's":[201],"effectiveness.":[202],"Furthermore,":[203],"results":[205],"underscore":[206],"ability":[208],"modules":[214],"reduce":[216],"representation":[217],"mitigate":[220],"systems.":[228]},"counts_by_year":[],"updated_date":"2026-06-16T07:32:37.131356","created_date":"2026-02-06T00:00:00"}