{"id":"https://openalex.org/W4402351677","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651506","title":"Cross-Aggregation Based Information Re-Enhancement for Recommendation","display_name":"Cross-Aggregation Based Information Re-Enhancement for Recommendation","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402351677","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651506"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651506","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651506","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","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/A5112370592","display_name":"Fangfei Li","orcid":null},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangfei Li","raw_affiliation_strings":["Shenzhen University,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen University,Shenzhen,China","institution_ids":["https://openalex.org/I180726961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005635945","display_name":"Zenghao Chen","orcid":"https://orcid.org/0000-0003-1673-2751"},"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":"Zenghao Chen","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"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":"35","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9926999807357788,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9842000007629395,"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":0.662108302116394}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.662108302116394}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651506","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651506","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W2097308346","https://openalex.org/W2117420919","https://openalex.org/W2154851992","https://openalex.org/W2156718197","https://openalex.org/W2887092413","https://openalex.org/W2907492528","https://openalex.org/W2945623882","https://openalex.org/W2945827670","https://openalex.org/W2962756421","https://openalex.org/W2964051675","https://openalex.org/W2996910652","https://openalex.org/W3012871709","https://openalex.org/W3044311607","https://openalex.org/W3045200674","https://openalex.org/W3080884086","https://openalex.org/W3094605801","https://openalex.org/W3097300053","https://openalex.org/W3100278010","https://openalex.org/W3100324210","https://openalex.org/W3104097132","https://openalex.org/W3153325943","https://openalex.org/W3155496675","https://openalex.org/W3172710079","https://openalex.org/W3177890934","https://openalex.org/W3204772083","https://openalex.org/W3207066264","https://openalex.org/W3209185641","https://openalex.org/W4200308080","https://openalex.org/W4224983022","https://openalex.org/W4294558607","https://openalex.org/W4310278871","https://openalex.org/W4319798365","https://openalex.org/W4321479942","https://openalex.org/W4321480047","https://openalex.org/W4321593635","https://openalex.org/W4380091476","https://openalex.org/W6638034506","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6779961489"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"Recently,":[0],"Graph":[1],"convolutional":[2,138],"networks":[3],"have":[4],"been":[5],"widely":[6],"and":[7,22,41,48,70,103,150],"effectively":[8],"applied":[9],"in":[10,19,30,115],"recommendation":[11,116],"systems.":[12],"However,":[13],"existing":[14,156],"research":[15],"still":[16],"faces":[17],"challenges":[18,47],"data":[20],"augmentation":[21],"lacks":[23],"sufficient":[24],"exploration":[25],"of":[26,33,39,52,136],"heterogeneous":[27,42,98],"information,":[28,54,108],"resulting":[29,89],"limited":[31],"extraction":[32],"information":[34,86,99],"from":[35,100],"a":[36,57],"combined":[37],"perspective":[38],"homogenous":[40,107],"structures.":[43],"To":[44],"address":[45],"these":[46],"improve":[49],"the":[50,92],"utilization":[51],"known":[53],"we":[55,143],"propose":[56],"novel":[58],"Cross-Aggregation":[59],"based":[60],"Information":[61],"Re-enhancement":[62],"for":[63,85],"Recommendation(CAIR).":[64],"This":[65],"method":[66,147],"utilizes":[67],"simple":[68],"\"dropout\"":[69],"residual":[71],"principles":[72],"to":[73,94,110],"learn":[74],"feature":[75],"representations":[76],"through":[77,148],"constructing":[78],"cross-aggregation":[79,122],"modules,":[80],"ultimately":[81],"extracting":[82],"effective":[83,112],"features":[84],"re-enhancement.":[87],"The":[88],"effect":[90],"enables":[91],"model":[93],"comprehensively":[95],"extract":[96],"valuable":[97],"node":[101],"neighborhoods":[102],"combine":[104],"it":[105,125,153],"with":[106],"leading":[109],"more":[111],"prediction":[113],"results":[114],"system":[117],"tasks.":[118],"Furthermore,":[119],"by":[120,133],"adopting":[121],"between":[123],"layers,":[124],"also":[126],"helps":[127],"alleviate":[128],"gradient":[129],"explosion":[130],"issues":[131],"caused":[132],"excessive":[134],"stacking":[135],"graph":[137],"layers.":[139],"In":[140],"this":[141],"paper,":[142],"extensively":[144],"validate":[145],"our":[146],"experiments":[149],"demonstrate":[151],"that":[152],"significantly":[154],"outperforms":[155],"techniques.":[157]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
