{"id":"https://openalex.org/W4406458185","doi":"https://doi.org/10.1109/bigdata62323.2024.10825764","title":"GRAINRec: Graph and Attention Integrated Approach for Real-Time Session-Based Item Recommendations","display_name":"GRAINRec: Graph and Attention Integrated Approach for Real-Time Session-Based Item Recommendations","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406458185","doi":"https://doi.org/10.1109/bigdata62323.2024.10825764"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825764","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825764","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5024865085","display_name":"Bhavtosh Rath","orcid":"https://orcid.org/0000-0001-8206-7396"},"institutions":[{"id":"https://openalex.org/I1320354487","display_name":"Target (United States)","ror":"https://ror.org/05mkyac79","country_code":"US","type":"company","lineage":["https://openalex.org/I1320354487"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Bhavtosh Rath","raw_affiliation_strings":["Target Corporation,Data Sciences,Brooklyn Park,MN,USA"],"affiliations":[{"raw_affiliation_string":"Target Corporation,Data Sciences,Brooklyn Park,MN,USA","institution_ids":["https://openalex.org/I1320354487"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029762759","display_name":"Pushkar Chennu","orcid":"https://orcid.org/0009-0001-8301-0575"},"institutions":[{"id":"https://openalex.org/I1320354487","display_name":"Target (United States)","ror":"https://ror.org/05mkyac79","country_code":"US","type":"company","lineage":["https://openalex.org/I1320354487"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pushkar Chennu","raw_affiliation_strings":["Target Corporation,Data Sciences,Brooklyn Park,MN,USA"],"affiliations":[{"raw_affiliation_string":"Target Corporation,Data Sciences,Brooklyn Park,MN,USA","institution_ids":["https://openalex.org/I1320354487"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103308965","display_name":"David Relyea","orcid":"https://orcid.org/0009-0006-8887-2939"},"institutions":[{"id":"https://openalex.org/I1320354487","display_name":"Target (United States)","ror":"https://ror.org/05mkyac79","country_code":"US","type":"company","lineage":["https://openalex.org/I1320354487"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Relyea","raw_affiliation_strings":["Target Corporation,Data Sciences,Brooklyn Park,MN,USA"],"affiliations":[{"raw_affiliation_string":"Target Corporation,Data Sciences,Brooklyn Park,MN,USA","institution_ids":["https://openalex.org/I1320354487"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103324230","display_name":"Prathyusha Kanmanth Reddy","orcid":"https://orcid.org/0009-0005-0725-5692"},"institutions":[{"id":"https://openalex.org/I1320354487","display_name":"Target (United States)","ror":"https://ror.org/05mkyac79","country_code":"US","type":"company","lineage":["https://openalex.org/I1320354487"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Prathyusha Kanmanth Reddy","raw_affiliation_strings":["Target Corporation,Data Sciences,Brooklyn Park,MN,USA"],"affiliations":[{"raw_affiliation_string":"Target Corporation,Data Sciences,Brooklyn Park,MN,USA","institution_ids":["https://openalex.org/I1320354487"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101976682","display_name":"Amit Pande","orcid":"https://orcid.org/0000-0002-5898-3404"},"institutions":[{"id":"https://openalex.org/I1320354487","display_name":"Target (United States)","ror":"https://ror.org/05mkyac79","country_code":"US","type":"company","lineage":["https://openalex.org/I1320354487"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Amit Pande","raw_affiliation_strings":["Target Corporation,Data Sciences,Brooklyn Park,MN,USA"],"affiliations":[{"raw_affiliation_string":"Target Corporation,Data Sciences,Brooklyn Park,MN,USA","institution_ids":["https://openalex.org/I1320354487"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5024865085"],"corresponding_institution_ids":["https://openalex.org/I1320354487"],"apc_list":null,"apc_paid":null,"fwci":1.6284,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.89090588,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2204","last_page":"2213"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":1.0,"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":1.0,"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":0.9980000257492065,"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":0.9976000189781189,"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/session","display_name":"Session (web analytics)","score":0.8406772613525391},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7697769403457642},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4355050325393677},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.26364070177078247},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.14320805668830872}],"concepts":[{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.8406772613525391},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7697769403457642},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4355050325393677},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26364070177078247},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.14320805668830872}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825764","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825764","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1652763299","https://openalex.org/W2040367556","https://openalex.org/W2139809240","https://openalex.org/W2171279286","https://openalex.org/W2415399965","https://openalex.org/W2474765392","https://openalex.org/W2507741081","https://openalex.org/W2773640334","https://openalex.org/W2783272285","https://openalex.org/W2809307135","https://openalex.org/W2899457523","https://openalex.org/W2963367478","https://openalex.org/W2963707260","https://openalex.org/W2963895127","https://openalex.org/W2963911286","https://openalex.org/W2964044287","https://openalex.org/W3034352512","https://openalex.org/W3035666843","https://openalex.org/W3080292067","https://openalex.org/W3093800735","https://openalex.org/W3101707147","https://openalex.org/W3185693672","https://openalex.org/W3214366753","https://openalex.org/W4287887094","https://openalex.org/W4295728955","https://openalex.org/W4297733535","https://openalex.org/W4299286960","https://openalex.org/W4315977496","https://openalex.org/W4360765128","https://openalex.org/W4367016446","https://openalex.org/W6692935382","https://openalex.org/W6779733120"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4230197055","https://openalex.org/W4296749040","https://openalex.org/W4404605447","https://openalex.org/W621808327","https://openalex.org/W644007644","https://openalex.org/W2497198634","https://openalex.org/W3012257603"],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,56,65,101,185,192],"session-based":[3,50],"recommendation":[4,51],"models":[5],"using":[6],"deep":[7],"learning":[8],"techniques":[9],"have":[10],"demonstrated":[11],"significant":[12],"performance":[13,205],"improvements.":[14],"While":[15],"they":[16,27],"can":[17],"enhance":[18],"model":[19,52,91,160,204],"sophistication":[20],"and":[21,47,189],"improve":[22],"the":[23,96,102,112,143,158],"relevance":[24],"of":[25,60,77,98,157,165,183],"recommendations,":[26],"also":[28,116,199],"make":[29],"it":[30],"challenging":[31],"to":[32,87,121,201],"implement":[33,122],"a":[34,45,69,118,176],"scalable":[35],"real-time":[36,123],"solution.":[37],"To":[38],"addressing":[39],"this":[40],"challenge,":[41],"we":[42],"propose":[43,117],"GRAINRec-":[44],"Graph":[46],"Attention":[48],"Integrated":[49],"that":[53,125],"generates":[54,92],"recommendations":[55,64,93,109,140,151],"real-time.":[57],"Our":[58],"scope":[59],"work":[61],"is":[62,71],"item":[63],"online":[66],"retail":[67],"where":[68],"session":[70,103,113,144],"defined":[72],"as":[73,82,111,142],"an":[74,162,181],"ordered":[75],"sequence":[76],"digital":[78],"guest":[79],"actions,":[80],"such":[81],"page":[83],"views":[84],"or":[85],"adds":[86],"cart.":[88],"The":[89,133],"proposed":[90,134,159],"by":[94],"considering":[95],"importance":[97],"all":[99,168],"items":[100],"together,":[104],"letting":[105],"us":[106,137],"predict":[107,138],"relevant":[108,139],"dynamically":[110,141],"evolves.":[114],"We":[115],"heuristic":[119],"approach":[120],"inferencing":[124],"meets":[126],"Target":[127],"platform's":[128],"service":[129],"level":[130],"agreement":[131],"(SLA).":[132],"architecture":[135],"lets":[136],"evolves,":[145],"rather":[146],"than":[147],"relying":[148],"on":[149],"pre-computed":[150],"for":[152,206],"each":[153],"item.":[154],"Evaluation":[155],"results":[156],"show":[161],"average":[163],"improvement":[164],"1.5%":[166],"across":[167],"offline":[169],"evaluation":[170],"metrics.":[171],"A/B":[172],"tests":[173],"done":[174,200],"over":[175],"2":[177],"week":[178],"duration":[179],"showed":[180],"increase":[182,191],"10%":[184],"click":[186],"through":[187],"rate":[188],"9%":[190],"attributable":[193],"demand.":[194],"Extensive":[195],"ablation":[196],"studies":[197],"are":[198],"understand":[202],"our":[203],"different":[207],"parameters.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}