{"id":"https://openalex.org/W4297165366","doi":"https://doi.org/10.48550/arxiv.2209.11471","title":"Modeling and Leveraging Prerequisite Context in Recommendation","display_name":"Modeling and Leveraging Prerequisite Context in Recommendation","publication_year":2022,"publication_date":"2022-09-23","ids":{"openalex":"https://openalex.org/W4297165366","doi":"https://doi.org/10.48550/arxiv.2209.11471"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2209.11471","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.11471","pdf_url":"https://arxiv.org/pdf/2209.11471","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2209.11471","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017709678","display_name":"Hengchang Hu","orcid":"https://orcid.org/0000-0001-7847-0641","observed_orcids":["https://orcid.org/0000-0001-7847-0641"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Hengchang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011007223","display_name":"Liangming Pan","orcid":"https://orcid.org/0000-0002-4636-397X","observed_orcids":["https://orcid.org/0000-0002-4636-397X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Liangming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034578337","display_name":"Yiding Ran","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ran, Yiding","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5066305082","display_name":"Min\u2010Yen Kan","orcid":"https://orcid.org/0000-0001-8507-3716","observed_orcids":["https://orcid.org/0000-0001-8507-3716"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kan, Min-Yen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.0,"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":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/recommender-systems","display_name":"recommender systems","score":0.7369999885559082},{"id":"https://openalex.org/keywords/context-aware-recommendation","display_name":"context-aware recommendation","score":0.8550000190734863},{"id":"https://openalex.org/keywords/cold-start-recommendation","display_name":"cold-start recommendation","score":0.6370000243186951}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8356460332870483},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6602896451950073},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6505123376846313},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.6362085342407227},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6120388507843018},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5981132388114929},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5217441320419312},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.42756080627441406},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.42286279797554016},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3564298152923584},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.34217238426208496},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.281521737575531},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2209.11471","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.11471","pdf_url":"https://arxiv.org/pdf/2209.11471","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2209.11471","doi":"https://doi.org/10.48550/arxiv.2209.11471","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2209.11471","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2209.11471","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2209.11471","pdf_url":"https://arxiv.org/pdf/2209.11471","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2553845148","https://openalex.org/W2760767748","https://openalex.org/W2514815078","https://openalex.org/W2748557612","https://openalex.org/W2788859551","https://openalex.org/W1996552124","https://openalex.org/W4226380897","https://openalex.org/W2398626440","https://openalex.org/W2082024599","https://openalex.org/W2892486560"],"abstract_inverted_index":{"Prerequisites":[0],"can":[1,77],"play":[2],"a":[3,87,103,119,133],"crucial":[4],"role":[5],"in":[6,158,173],"users'":[7],"decision-making":[8],"yet":[9],"recommendation":[10,21,146],"systems":[11,22],"have":[12],"not":[13],"fully":[14],"utilized":[15],"such":[16],"contextual":[17,41],"background":[18],"knowledge.":[19],"Traditional":[20],"(RS)":[23],"mostly":[24],"enrich":[25],"user-item":[26],"interactions":[27],"where":[28,91],"the":[29,40,57,60,142],"context":[30,72,93],"consists":[31],"of":[32,136,166,178],"static":[33],"user":[34,61],"profiles":[35],"and":[36,43,145],"item":[37,55,66],"descriptions,":[38],"ignoring":[39],"logic":[42],"constraints":[44],"that":[45,59],"underlie":[46],"them.":[47],"For":[48],"example,":[49],"an":[50,54,163],"RS":[51],"may":[52],"recommend":[53],"on":[56],"condition":[58],"has":[62],"interacted":[63],"with":[64,176],"another":[65],"as":[67],"its":[68],"prerequisite.":[69],"Modeling":[70],"prerequisite":[71,92,122,143],"from":[73],"conceptual":[74],"side":[75],"information":[76],"overcome":[78],"this":[79],"weakness.":[80],"We":[81,100,129],"propose":[82],"Prerequisite":[83,104],"Driven":[84],"Recommendation":[85],"(PDR),":[86],"generic":[88],"context-aware":[89],"framework":[90],"is":[94],"explicitly":[95],"modeled":[96],"to":[97,109,180],"facilitate":[98],"recommendation.":[99],"first":[101],"design":[102],"Knowledge":[105],"Linking":[106],"(PKL)":[107],"algorithm,":[108],"curate":[110],"datasets":[111],"facilitating":[112],"PDR":[113],"research.":[114],"Employing":[115],"it,":[116],"we":[117,151],"build":[118],"75k+":[120],"high-quality":[121],"concept":[123],"dataset":[124],"which":[125],"spans":[126],"three":[127,160],"domains.":[128],"then":[130],"contribute":[131],"PDRS,":[132],"neural":[134],"instantiation":[135],"PDR.":[137],"By":[138],"jointly":[139],"optimizing":[140],"both":[141],"learning":[144],"tasks":[147],"through":[148],"multi-layer":[149],"perceptrons,":[150],"find":[152],"PDRS":[153,169],"consistently":[154],"outperforms":[155],"baseline":[156],"models":[157],"all":[159],"domains,":[161],"by":[162],"average":[164],"margin":[165],"7.41%.":[167],"Importantly,":[168],"performs":[170],"especially":[171],"well":[172],"cold-start":[174],"scenarios":[175],"improvements":[177],"up":[179],"17.65%.":[181]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2022-09-27T00:00:00"}
