{"id":"https://openalex.org/W4385567834","doi":"https://doi.org/10.1145/3580305.3599450","title":"On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering","display_name":"On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385567834","doi":"https://doi.org/10.1145/3580305.3599450"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599450","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599450","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","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/A5067015458","display_name":"Jiayan Guo","orcid":"https://orcid.org/0000-0002-7741-1153"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiayan Guo","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7741-1153","affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008387608","display_name":"Lun Du","orcid":"https://orcid.org/0000-0002-7625-0650"},"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":"Lun Du","raw_affiliation_strings":["Microsoft, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7625-0650","affiliations":[{"raw_affiliation_string":"Microsoft, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100711699","display_name":"Xu Chen","orcid":"https://orcid.org/0000-0001-5041-0532"},"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":"Xu Chen","raw_affiliation_strings":["Microsoft, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-5041-0532","affiliations":[{"raw_affiliation_string":"Microsoft, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052953923","display_name":"Xiaojun Ma","orcid":"https://orcid.org/0000-0001-6757-3055"},"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":"Xiaojun Ma","raw_affiliation_strings":["Microsoft, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6757-3055","affiliations":[{"raw_affiliation_string":"Microsoft, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086820941","display_name":"Qiang Fu","orcid":"https://orcid.org/0000-0002-5821-7267"},"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":"Qiang Fu","raw_affiliation_strings":["Microsoft, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5821-7267","affiliations":[{"raw_affiliation_string":"Microsoft, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006300825","display_name":"Shi Han","orcid":"https://orcid.org/0000-0002-0360-6089"},"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":"Shi Han","raw_affiliation_strings":["Microsoft, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0360-6089","affiliations":[{"raw_affiliation_string":"Microsoft, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100331488","display_name":"Dongmei Zhang","orcid":"https://orcid.org/0000-0002-9230-2799"},"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":"Dongmei Zhang","raw_affiliation_strings":["Microsoft, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9230-2799","affiliations":[{"raw_affiliation_string":"Microsoft, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022499183","display_name":"Yan Zhang","orcid":"https://orcid.org/0000-0003-4003-0290"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zhang","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4003-0290","affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5067015458"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":9.2775,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.97915138,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"602","last_page":"613"},"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":0.9959999918937683,"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/T11478","display_name":"Caching and Content Delivery","score":0.986299991607666,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.7660456895828247},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.7418409585952759},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.6667402982711792},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6608083844184875},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6357294321060181},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.5014739036560059},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.48065054416656494},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3867546319961548},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3582318425178528},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3414098620414734},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32910972833633423}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7660456895828247},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.7418409585952759},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.6667402982711792},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6608083844184875},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6357294321060181},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5014739036560059},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.48065054416656494},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3867546319961548},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3582318425178528},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3414098620414734},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32910972833633423},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599450","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599450","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4099999964237213,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1965847964","https://openalex.org/W2027731328","https://openalex.org/W2069065514","https://openalex.org/W2110953678","https://openalex.org/W2161902954","https://openalex.org/W2512971201","https://openalex.org/W2583674722","https://openalex.org/W2605350416","https://openalex.org/W2741249238","https://openalex.org/W2796431263","https://openalex.org/W2912636151","https://openalex.org/W2913754224","https://openalex.org/W2945827670","https://openalex.org/W2946617802","https://openalex.org/W2964105372","https://openalex.org/W2984100107","https://openalex.org/W3044311607","https://openalex.org/W3045200674","https://openalex.org/W3046435230","https://openalex.org/W3081170586","https://openalex.org/W3094127838","https://openalex.org/W3100278010","https://openalex.org/W3100324210","https://openalex.org/W3175753490","https://openalex.org/W3208227120","https://openalex.org/W3211009588","https://openalex.org/W3211143493","https://openalex.org/W3211258672","https://openalex.org/W4220909642","https://openalex.org/W4284680110","https://openalex.org/W4284688665","https://openalex.org/W4284704639","https://openalex.org/W4287115771","https://openalex.org/W4306317072"],"related_works":["https://openalex.org/W1484355083","https://openalex.org/W2772628444","https://openalex.org/W4220714703","https://openalex.org/W2735929803","https://openalex.org/W2170391450","https://openalex.org/W2098758514","https://openalex.org/W3008845055","https://openalex.org/W2041004656","https://openalex.org/W135044020","https://openalex.org/W4376854386"],"abstract_inverted_index":{"Collaborative":[0],"filtering":[1,67],"(CF)":[2],"is":[3,62,174],"an":[4,124],"important":[5,105],"research":[6],"direction":[7],"in":[8,30,37,41,183],"recommender":[9],"systems":[10],"that":[11,107,172],"aims":[12],"to":[13,34,64,81,85,88,113],"make":[14],"recommendations":[15,185],"given":[16],"the":[17,42,53,97,104,119,149,154,167],"information":[18,40],"on":[19,118,132,142,164],"user-item":[20,43],"interactions.":[21],"Graph":[22],"CF":[23,61,130],"has":[24],"attracted":[25],"more":[26,28],"and":[27,83,126,136,151],"attention":[29],"recent":[31,50],"years":[32],"due":[33],"its":[35,65],"effectiveness":[36,150],"leveraging":[38],"high-order":[39],"bipartite":[44],"graph":[45,56,109],"for":[46,60,129,186],"better":[47,115,175],"recommendations.":[48],"Specifically,":[49],"studies":[51],"show":[52,148,171],"success":[54],"of":[55,75,99,153],"neural":[57],"networks":[58],"(GNN)":[59],"attributed":[63],"low-pass":[66],"effects.":[68],"However,":[69],"current":[70],"researches":[71],"lack":[72],"a":[73,108],"study":[74],"how":[76,84],"different":[77],"signal":[78],"components":[79],"contributes":[80],"recommendations,":[82],"design":[86,122],"strategies":[87],"properly":[89],"use":[90],"them":[91],"well.":[92],"To":[93],"this":[94],"end,":[95],"from":[96],"view":[98],"spectral":[100],"transformation,":[101],"we":[102,121],"analyze":[103],"factors":[106],"filter":[110],"should":[111],"consider":[112],"achieve":[114],"performance.":[116],"Based":[117],"discoveries,":[120],"JGCF,":[123],"efficient":[125],"effective":[127],"method":[128],"based":[131],"Jacobi":[133],"polynomial":[134],"bases":[135],"frequency":[137],"decomposition":[138],"strategies.":[139],"Extensive":[140],"experiments":[141],"four":[143],"widely":[144],"used":[145],"public":[146],"datasets":[147],"efficiency":[152],"proposed":[155],"methods,":[156],"which":[157,180],"brings":[158],"at":[159,176],"most":[160],"27.06%":[161],"performance":[162],"gain":[163],"Alibaba-iFashion.":[165],"Besides,":[166],"experimental":[168],"results":[169],"also":[170],"JGCF":[173],"handling":[177],"sparse":[178],"datasets,":[179],"shows":[181],"potential":[182],"making":[184],"cold-start":[187],"users.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":3}],"updated_date":"2026-05-12T08:28:47.272897","created_date":"2025-10-10T00:00:00"}
