{"id":"https://openalex.org/W4390548148","doi":"https://doi.org/10.1145/3632410.3632433","title":"New User Product Recommendation for Q-commerce via Hierarchical Cross-Domain Learning","display_name":"New User Product Recommendation for Q-commerce via Hierarchical Cross-Domain Learning","publication_year":2024,"publication_date":"2024-01-03","ids":{"openalex":"https://openalex.org/W4390548148","doi":"https://doi.org/10.1145/3632410.3632433"},"language":"en","primary_location":{"id":"doi:10.1145/3632410.3632433","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3632410.3632433","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)","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/A5034187458","display_name":"B. Chawla","orcid":"https://orcid.org/0000-0002-8277-2893"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bhavi Chawla","raw_affiliation_strings":["Swiggy, India"],"raw_orcid":"https://orcid.org/0000-0002-8277-2893","affiliations":[{"raw_affiliation_string":"Swiggy, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062350008","display_name":"Shubha Shedthikere","orcid":"https://orcid.org/0009-0006-5775-3443"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shubha Shedthikere","raw_affiliation_strings":["Swiggy, India"],"raw_orcid":"https://orcid.org/0009-0006-5775-3443","affiliations":[{"raw_affiliation_string":"Swiggy, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090594082","display_name":"Jairaj Sathyanarayana","orcid":"https://orcid.org/0009-0005-0083-8502"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jairaj Sathyanarayana","raw_affiliation_strings":["Swiggy, India"],"raw_orcid":"https://orcid.org/0009-0005-0083-8502","affiliations":[{"raw_affiliation_string":"Swiggy, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034187458"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.73,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.93260567,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"384","last_page":"392"},"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9853000044822693,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9708999991416931,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/leverage","display_name":"Leverage (statistics)","score":0.8159377574920654},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7563645839691162},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.682758629322052},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.6469842195510864},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.5679377913475037},{"id":"https://openalex.org/keywords/e-commerce","display_name":"E-commerce","score":0.49175766110420227},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4332968592643738},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4113750457763672},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.3502779006958008},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.3381149172782898},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2593897581100464}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.8159377574920654},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7563645839691162},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.682758629322052},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6469842195510864},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.5679377913475037},{"id":"https://openalex.org/C78597825","wikidata":"https://www.wikidata.org/wiki/Q484847","display_name":"E-commerce","level":2,"score":0.49175766110420227},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4332968592643738},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4113750457763672},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3502779006958008},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3381149172782898},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2593897581100464},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3632410.3632433","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3632410.3632433","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)","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":9,"referenced_works":["https://openalex.org/W2042281163","https://openalex.org/W2054141820","https://openalex.org/W2155106456","https://openalex.org/W2604167613","https://openalex.org/W2608239929","https://openalex.org/W2767781968","https://openalex.org/W3188301284","https://openalex.org/W4205171160","https://openalex.org/W4301312111"],"related_works":["https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W4246980185","https://openalex.org/W2150182025","https://openalex.org/W4317039510","https://openalex.org/W3197542405","https://openalex.org/W3125580266","https://openalex.org/W3092950680","https://openalex.org/W2600924427","https://openalex.org/W2039241249"],"abstract_inverted_index":{"In":[0,69],"this":[1],"work,":[2],"we":[3,191],"address":[4],"the":[5,12,46,54,66,78,86,90,101,104,127,130,134,144,150,166,201,205,223,237,247,251,256],"new":[6,36,147],"user":[7],"cold":[8],"start":[9],"problem":[10],"in":[11,65,133,149,204,222,236,264,268,275,277],"product":[13,219,234],"recommendation":[14,24,50,116],"system":[15],"for":[16,35,146],"a":[17,57,109,122,193,241],"quick-commerce":[18],"(q-commerce)":[19],"grocery":[20,92,224,238],"delivery":[21,93],"service.":[22],"Traditional":[23],"systems":[25,117],"built":[26],"on":[27,114],"user-product":[28],"interaction":[29],"data":[30,40,55,102],"tend":[31],"to":[32,39,45,62,89,99,142,175,185,232],"perform":[33],"poorly":[34],"users":[37,148],"owing":[38],"sparsity.":[41],"This":[42,96,169],"has":[43],"led":[44],"emergence":[47],"of":[48,77,82,112,266],"cross-domain":[49,115,123,197],"systems,":[51],"which":[52,118,199,282],"leverage":[53,100],"from":[56,103],"relatively":[58],"richer":[59],"\u2018source\u2019":[60],"domain":[61,88,239],"improve":[63],"recommendations":[64,235],"target":[67,151],"domain.":[68,106,152,225],"our":[70,83,286],"case,":[71],"online":[72,91,279],"food":[73,105,215],"delivery,":[74],"being":[75],"one":[76],"more":[79],"evolved":[80],"services":[81],"platform,":[84],"is":[85,108,163,170,283],"source":[87],"referenced":[94],"above.":[95],"enables":[97],"us":[98],"There":[107],"large":[110],"body":[111],"literature":[113],"typically":[119],"involves":[120],"learning":[121],"mapping":[124,140,211],"function":[125,141],"between":[126,213],"customer":[128],"or":[129],"item":[131,206],"embeddings":[132,145,167],"two":[135],"domains":[136],"and":[137,181,208,218,255,261,272],"leveraging":[138],"that":[139,155,246],"derive":[143],"We":[153,244],"show":[154,245],"such":[156],"approaches":[157],"are":[158,229],"sub-optimal":[159],"where":[160,178],"sales":[161],"distribution":[162],"long-tailed,":[164],"making":[165],"noisy.":[168],"further":[171],"aggravated":[172],"when":[173],"applied":[174],"q-commerce":[176],"settings":[177],"location-specific":[179],"geographical":[180],"cultural":[182],"diversities":[183],"have":[184],"be":[186],"considered.":[187],"Given":[188],"these":[189],"nuances,":[190],"propose":[192],"neural":[194],"network-based":[195],"hierarchical":[196],"mapper,":[198],"leverages":[200],"inherent":[202],"hierarchy":[203],"taxonomy":[207],"learns":[209],"multiple":[210],"functions":[212],"customers\u2019":[214],"category":[216,220],"preferences":[217,221,228],"These":[226],"category-level":[227],"then":[230],"used":[231],"personalize":[233],"through":[240],"learning-to-rank":[242],"model.":[243],"proposed":[248],"algorithm":[249],"outperforms":[250],"embedding":[252],"mapping-based":[253],"approach":[254],"popularity-based":[257],"baseline":[258],"by":[259],"30%":[260],"8%":[262],"respectively,":[263],"terms":[265],"NDCG":[267],"an":[269,278],"offline":[270],"experiment":[271,281],"4%":[273],"improvement":[274],"conversion":[276],"A/B":[280],"significant":[284],"at":[285],"scale.":[287]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}