{"id":"https://openalex.org/W7129103501","doi":"https://doi.org/10.1145/3773966.3777951","title":"DDGCL: Dual Diffusion-based Graph Contrastive Learning for Recommendation","display_name":"DDGCL: Dual Diffusion-based Graph Contrastive Learning for Recommendation","publication_year":2026,"publication_date":"2026-02-16","ids":{"openalex":"https://openalex.org/W7129103501","doi":"https://doi.org/10.1145/3773966.3777951"},"language":null,"primary_location":{"id":"doi:10.1145/3773966.3777951","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773966.3777951","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3773966.3777951","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102191950","display_name":"Shiqi Ge","orcid":null},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shiqi Ge","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059161616","display_name":"Shunmei Meng","orcid":"https://orcid.org/0000-0002-6173-9787"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shunmei Meng","raw_affiliation_strings":["Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090999741","display_name":"Xiaoxiao Chi","orcid":"https://orcid.org/0000-0002-8607-6502"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xiaoxiao Chi","raw_affiliation_strings":["Macquarie University, Sydney, New South Wales, Australia"],"affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, New South Wales, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126082055","display_name":"Lianyong Qi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lianyong Qi","raw_affiliation_strings":["China University of Petroleum, Qingdao, China"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum, Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121634185","display_name":"Xiaolong Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I200845125","display_name":"Nanjing University of Information Science and Technology","ror":"https://ror.org/02y0rxk19","country_code":"CN","type":"education","lineage":["https://openalex.org/I200845125"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaolong Xu","raw_affiliation_strings":["Nanjing University of Information Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"Nanjing University of Information Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I200845125"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126148065","display_name":"Amin Beheshti","orcid":null},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Amin Beheshti","raw_affiliation_strings":["Macquarie University, Sydney, New South Wales, Australia"],"affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, New South Wales, Australia","institution_ids":["https://openalex.org/I99043593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124237716","display_name":"Xuyun Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xuyun Zhang","raw_affiliation_strings":["Macquarie University, Sydney, New South Wales, Australia"],"affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, New South Wales, Australia","institution_ids":["https://openalex.org/I99043593"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5102191950"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.82117601,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"489","last_page":"497"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.5820000171661377,"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":0.5820000171661377,"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":0.2524000108242035,"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/T14413","display_name":"Advanced Technologies in Various Fields","score":0.05050000175833702,"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/graph","display_name":"Graph","score":0.6259999871253967},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.5216000080108643},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5048999786376953},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.34150001406669617},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3384000062942505},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3334999978542328},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3303000032901764}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7407000064849854},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6259999871253967},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5515000224113464},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.5216000080108643},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5048999786376953},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45419999957084656},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.367900013923645},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3303000032901764},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3257000148296356},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30820000171661377},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.28780001401901245},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2718999981880188},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.263700008392334},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3773966.3777951","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773966.3777951","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3773966.3777951","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3773966.3777951","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2054141820","https://openalex.org/W2101409192","https://openalex.org/W2997261254","https://openalex.org/W3044311607","https://openalex.org/W3045200674","https://openalex.org/W3094605801","https://openalex.org/W4220909642","https://openalex.org/W4224983022","https://openalex.org/W4284701290","https://openalex.org/W4386867884","https://openalex.org/W4403421847","https://openalex.org/W4403577911","https://openalex.org/W4403582372","https://openalex.org/W4407953198"],"related_works":[],"abstract_inverted_index":{"Contrastive":[0],"learning":[1,79],"has":[2],"emerged":[3],"as":[4,157],"a":[5,74,134,148],"promising":[6],"paradigm":[7],"by":[8],"inherently":[9],"generating":[10],"self-supervised":[11],"signals":[12],"and":[13,42,118],"uncovering":[14],"latent":[15],"patterns":[16],"from":[17,51],"interaction":[18,99],"data":[19],"to":[20,57,93,123,161],"enhance":[21],"recommendation":[22,30,65],"performance.":[23],"However,":[24],"most":[25],"current":[26],"graph":[27,39,77,129,154],"contrastive":[28,78,82,125],"learning-based":[29],"methods":[31],"rely":[32],"on":[33,97,120,177],"random":[34],"augmentation":[35,138],"strategies,which":[36],"may":[37],"disrupt":[38],"structural":[40,104,130],"information":[41,155],"compromise":[43],"model":[44,113],"robustness.":[45],"In":[46,132],"addition,":[47,133],"long-tail":[48,141,167],"items":[49,142],"suffer":[50],"insufficient":[52],"exposure,":[53],"making":[54],"it":[55],"difficult":[56],"learn":[58],"high-quality":[59],"feature":[60],"rep-":[61],"resentations,":[62],"ultimately":[63],"degrading":[64],"effectiveness.To":[66],"overcome":[67],"these":[68],"limitations,":[69],"this":[70,121],"paper":[71],"presents":[72],"DDGCL,":[73],"dual":[75],"diffusion-based":[76],"method.":[80],"A":[81,174],"view":[83],"optimization":[84],"module":[85,146],"is":[86,143],"designed,":[87],"which":[88],"employs":[89],"singular":[90],"value":[91],"decomposition":[92],"perform":[94],"low-rank":[95],"approximation":[96],"the":[98,108,163,191,194],"graph,":[100],"efficiently":[101],"extracting":[102],"global":[103,153],"features":[105],"while":[106],"accelerating":[107],"diffusion":[109,112,150],"process.":[110],"The":[111],"then":[114],"performs":[115],"noise":[116],"addition":[117],"denoising":[119,164],"basis":[122],"generate":[124],"views":[126],"that":[127,183],"preserve":[128],"information.":[131],"method":[135],"for":[136,140],"embedding":[137],"designed":[139],"proposed.":[144],"This":[145],"utilizes":[147],"conditional":[149,158],"model,":[151],"where":[152],"serves":[156],"con-":[159],"straints":[160],"guide":[162],"process":[165],"of":[166,193],"items,":[168],"thereby":[169],"improving":[170],"their":[171],"representation":[172],"learning.":[173],"comprehensive":[175],"evaluation":[176],"multiple":[178],"public":[179],"benchmark":[180],"datasets":[181],"demonstrates":[182],"DDGCL":[184],"significantly":[185],"outperforms":[186],"various":[187],"baseline":[188],"models,":[189],"validating":[190],"effectiveness":[192],"proposed":[195],"approach.":[196]},"counts_by_year":[],"updated_date":"2026-02-18T06:20:13.636215","created_date":"2026-02-17T00:00:00"}
