{"id":"https://openalex.org/W4408355489","doi":"https://doi.org/10.1109/icassp49660.2025.10889304","title":"E-RNS : Enhancing Negative Sample Quality from Gradient Perspective for Graph Recommendation","display_name":"E-RNS : Enhancing Negative Sample Quality from Gradient Perspective for Graph Recommendation","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408355489","doi":"https://doi.org/10.1109/icassp49660.2025.10889304"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10889304","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889304","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5070286974","display_name":"Qian Feng","orcid":"https://orcid.org/0000-0001-7655-2600"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiangsheng Feng","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086121472","display_name":"Jiwei Qin","orcid":"https://orcid.org/0000-0001-7023-2630"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiwei Qin","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028061893","display_name":"Jie Ma","orcid":"https://orcid.org/0000-0003-4932-9205"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Ma","raw_affiliation_strings":["Xinjiang University,School of Computer Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xinjiang University,School of Computer Science and Technology","institution_ids":["https://openalex.org/I96908189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"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":0.01719682,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9902999997138977,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9902999997138977,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9707000255584717,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9645000100135803,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6307777762413025},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.5941164493560791},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4987061023712158},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4968571960926056},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.442199170589447},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24958783388137817},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1932424008846283},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.06979486346244812},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.05716553330421448}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6307777762413025},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.5941164493560791},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4987061023712158},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4968571960926056},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.442199170589447},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24958783388137817},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1932424008846283},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.06979486346244812},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.05716553330421448},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10889304","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889304","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4399999976158142,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W2100235918","https://openalex.org/W2101409192","https://openalex.org/W2102035799","https://openalex.org/W2110953678","https://openalex.org/W2219888463","https://openalex.org/W2624407581","https://openalex.org/W2640408555","https://openalex.org/W2945827670","https://openalex.org/W3045200674","https://openalex.org/W3080456792","https://openalex.org/W3094605801","https://openalex.org/W4220909642","https://openalex.org/W4224325005","https://openalex.org/W4283702870","https://openalex.org/W4284666445","https://openalex.org/W4381786098","https://openalex.org/W4385825460","https://openalex.org/W4400524976","https://openalex.org/W4402799683","https://openalex.org/W6631943919","https://openalex.org/W6680830989","https://openalex.org/W6753331806","https://openalex.org/W6782333661","https://openalex.org/W6783961830"],"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/W2018871932","https://openalex.org/W2001405890"],"abstract_inverted_index":{"Bayesian":[0],"Personalized":[1],"Ranking":[2],"(BPR)":[3],"is":[4,141],"a":[5,74,112],"widely":[6],"used":[7],"optimization":[8],"function":[9],"in":[10,45,95,101,117,133],"GNN-based":[11,160],"recommender":[12,161],"systems,":[13],"and":[14,65,110],"negative":[15,66,76,89,98,145,151],"samples":[16,40,90,127,146],"are":[17],"usually":[18],"obtained":[19],"through":[20],"the":[21,32,57,93,102,118,122,134,144],"Random":[22,82],"Negative":[23,83],"Sampling":[24,84],"(RNS)":[25],"method":[26],"during":[27],"BPR":[28,54],"training.":[29],"However,":[30],"from":[31],"gradient":[33],"perspective,":[34],"RNS":[35],"tends":[36],"to":[37,53,60,128,147,159],"select":[38],"low-quality":[39],"with":[41,143],"minimal":[42],"information,":[43],"resulting":[44],"small":[46,49],"gradients.":[47],"These":[48],"gradients":[50],"contribute":[51],"little":[52],"optimization,":[55],"limiting":[56],"model\u2019s":[58],"ability":[59],"effectively":[61],"distinguish":[62],"between":[63],"positive":[64,126],"samples.":[67,99,152],"To":[68],"alleviate":[69],"this":[70,138],"issue,":[71],"we":[72],"propose":[73],"general":[75],"sample":[77],"information":[78,94,140],"enhancement":[79],"method:":[80],"Enhancing":[81],"(E-RNS),":[85],"which":[86],"constructs":[87],"hard":[88,150],"by":[91],"enhancing":[92],"randomly":[96],"selected":[97],"Specifically,":[100],"Noise":[103],"Injection":[104],"step,":[105,137],"it":[106],"generates":[107],"initial":[108],"noise":[109,116],"injects":[111],"certain":[113],"amount":[114],"of":[115,125],"same":[119],"direction":[120],"into":[121],"vector":[123],"dimensions":[124],"create":[129],"enriched":[130,139],"information.":[131],"Then,":[132],"Information":[135],"Fusion":[136],"mixed":[142],"synthesize":[148],"new":[149],"Extensive":[153],"experiments":[154],"demonstrate":[155],"that":[156],"applying":[157],"E-RNS":[158],"models":[162],"significantly":[163],"improves":[164],"performance.":[165]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}