{"id":"https://openalex.org/W7155534034","doi":"https://doi.org/10.1145/3799830.3799870","title":"Multi-Stage RecSys with Gated-VIC 2T: GFLU-Enhanced Pre-Ranking Meets VICReg","display_name":"Multi-Stage RecSys with Gated-VIC 2T: GFLU-Enhanced Pre-Ranking Meets VICReg","publication_year":2025,"publication_date":"2025-12-17","ids":{"openalex":"https://openalex.org/W7155534034","doi":"https://doi.org/10.1145/3799830.3799870"},"language":null,"primary_location":{"id":"doi:10.1145/3799830.3799870","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799830.3799870","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 13th ACM IKDD International Conference on Data Science","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3799830.3799870","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113127308","display_name":"Manu Joseph","orcid":null},"institutions":[{"id":"https://openalex.org/I1330693074","display_name":"Walmart (United States)","ror":"https://ror.org/04j0gge90","country_code":"US","type":"company","lineage":["https://openalex.org/I1330693074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Manu Joseph","raw_affiliation_strings":["Walmart Global Tech, BANGALORE, Mobile, India"],"raw_orcid":"https://orcid.org/0000-0002-6358-2381","affiliations":[{"raw_affiliation_string":"Walmart Global Tech, BANGALORE, Mobile, India","institution_ids":["https://openalex.org/I1330693074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5113127308"],"corresponding_institution_ids":["https://openalex.org/I1330693074"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.87260032,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"233","last_page":"242"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9506999850273132,"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":0.9506999850273132,"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/T13274","display_name":"Expert finding and Q&A systems","score":0.006500000134110451,"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":0.00559999980032444,"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/recommender-system","display_name":"Recommender system","score":0.6482999920845032},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6114000082015991},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.5687999725341797},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5450999736785889},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5315999984741211},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5273000001907349},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.40220001339912415},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.3783000111579895}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6789000034332275},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6482999920845032},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6114000082015991},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.5687999725341797},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5450999736785889},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5372999906539917},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5315999984741211},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5273000001907349},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5040000081062317},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.40220001339912415},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3783000111579895},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3555999994277954},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3391000032424927},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.30320000648498535},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.27869999408721924},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C2984074130","wikidata":"https://www.wikidata.org/wiki/Q73539779","display_name":"R package","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.25450000166893005},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3799830.3799870","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799830.3799870","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 13th ACM IKDD International Conference on Data Science","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3799830.3799870","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3799830.3799870","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 13th ACM IKDD International Conference on Data Science","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W1678356000","https://openalex.org/W2045871438","https://openalex.org/W2074694452","https://openalex.org/W2136189984","https://openalex.org/W2140942692","https://openalex.org/W2144807535","https://openalex.org/W2171590421","https://openalex.org/W2295598076","https://openalex.org/W2555759795","https://openalex.org/W2783774207","https://openalex.org/W2789758093","https://openalex.org/W2964199361","https://openalex.org/W2972801466","https://openalex.org/W2998702515","https://openalex.org/W3035524453","https://openalex.org/W4284707446","https://openalex.org/W4382703380","https://openalex.org/W4384828614"],"related_works":[],"abstract_inverted_index":{"In":[0],"any":[1],"marketplace,":[2],"we":[3,38],"have":[4],"a":[5,78],"hub":[6],"for":[7],"third-party":[8],"sellers":[9],"to":[10,28,32],"manage":[11],"their":[12],"offerings":[13],"and":[14,62,86,95,103],"access":[15],"strategic":[16],"insights.":[17],"However,":[18],"the":[19,40,47,55,83],"abundance":[20],"of":[21],"information":[22],"across":[23],"mutiple":[24],"recommendation":[25,93],"channels":[26],"leads":[27],"low":[29],"engagement":[30],"due":[31],"its":[33],"complexity.":[34],"To":[35],"address":[36],"this,":[37],"propose":[39],"Multi-Stage":[41],"Personalized":[42],"Recommendation":[43],"System":[44],"(MS-RecSys),":[45],"featuring":[46],"innovative":[48],"Gated-VIC":[49],"2T":[50],"Model.":[51],"This":[52],"model":[53],"enhances":[54],"Pre-Ranking":[56],"Stage":[57,85],"by":[58],"integrating":[59],"Variance,":[60],"Invariance,":[61],"Covariance":[63],"regularization":[64],"with":[65],"Gated":[66],"Feature":[67],"Learning":[68],"Units,":[69],"surpassing":[70],"traditional":[71],"approaches":[72],"in":[73,82,106],"capturing":[74],"user-item":[75],"interactions.":[76],"Additionally,":[77],"Multi-Layered":[79],"Ensemble":[80],"system":[81],"Ranking":[84],"an":[87],"optimized":[88],"inference":[89],"pipeline":[90],"significantly":[91],"improve":[92],"relevance":[94],"processing":[96],"efficiency.":[97],"Our":[98],"offline":[99],"evaluations":[100],"show":[101],"consistent":[102],"considerable":[104],"improvement":[105],"user":[107],"engagement,":[108],"ranking":[109],"precision,and":[110],"diversity":[111],"metrics.":[112]},"counts_by_year":[],"updated_date":"2026-04-25T06:06:54.107920","created_date":"2026-04-25T00:00:00"}
