{"id":"https://openalex.org/W4409149849","doi":"https://doi.org/10.1145/3690624.3709329","title":"CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship","display_name":"CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship","publication_year":2025,"publication_date":"2025-04-04","ids":{"openalex":"https://openalex.org/W4409149849","doi":"https://doi.org/10.1145/3690624.3709329"},"language":"en","primary_location":{"id":"doi:10.1145/3690624.3709329","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3690624.3709329","pdf_url":null,"source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3690624.3709329","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028740930","display_name":"Yeon-Chang Lee","orcid":"https://orcid.org/0000-0002-8769-0678"},"institutions":[{"id":"https://openalex.org/I48566637","display_name":"Ulsan National Institute of Science and Technology","ror":"https://ror.org/017cjz748","country_code":"KR","type":"education","lineage":["https://openalex.org/I48566637"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Yeon-Chang Lee","raw_affiliation_strings":["Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea","institution_ids":["https://openalex.org/I48566637"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019077347","display_name":"JaeHyun Lee","orcid":"https://orcid.org/0009-0000-0797-3946"},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"JaeHyun Lee","raw_affiliation_strings":["Hanyang University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Hanyang University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I4575257"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006093397","display_name":"Michiharu Yamashita","orcid":"https://orcid.org/0009-0002-3802-8618"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michiharu Yamashita","raw_affiliation_strings":["The Pennsylvania State University, University Park, PA, USA"],"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100405086","display_name":"Dongwon Lee","orcid":"https://orcid.org/0000-0001-8371-7629"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dongwon Lee","raw_affiliation_strings":["The Pennsylvania State University, University Park, PA, USA"],"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100656150","display_name":"Sang\u2010Wook Kim","orcid":"https://orcid.org/0000-0002-6345-9084"},"institutions":[{"id":"https://openalex.org/I4575257","display_name":"Hanyang University","ror":"https://ror.org/046865y68","country_code":"KR","type":"education","lineage":["https://openalex.org/I4575257"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sang-Wook Kim","raw_affiliation_strings":["Hanyang University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Hanyang University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I4575257"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5028740930"],"corresponding_institution_ids":["https://openalex.org/I48566637"],"apc_list":null,"apc_paid":null,"fwci":4.6722,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.93623157,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"647","last_page":"658"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12401","display_name":"Scheduling and Timetabling Solutions","score":0.9545999765396118,"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"}},"topics":[{"id":"https://openalex.org/T12401","display_name":"Scheduling and Timetabling Solutions","score":0.9545999765396118,"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/T13812","display_name":"AI and HR Technologies","score":0.9287999868392944,"subfield":{"id":"https://openalex.org/subfields/1407","display_name":"Organizational Behavior and Human Resource Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7191423177719116},{"id":"https://openalex.org/keywords/ternary-operation","display_name":"Ternary operation","score":0.6751662492752075},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5817468166351318},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.515352189540863},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41219577193260193},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3218345642089844},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.27747759222984314},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.062380969524383545},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.052599549293518066}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7191423177719116},{"id":"https://openalex.org/C64452783","wikidata":"https://www.wikidata.org/wiki/Q1524945","display_name":"Ternary operation","level":2,"score":0.6751662492752075},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5817468166351318},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.515352189540863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41219577193260193},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3218345642089844},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27747759222984314},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.062380969524383545},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.052599549293518066},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3690624.3709329","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3690624.3709329","pdf_url":null,"source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3690624.3709329","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3690624.3709329","pdf_url":null,"source":null,"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":71,"referenced_works":["https://openalex.org/W629652611","https://openalex.org/W2011669536","https://openalex.org/W2048587746","https://openalex.org/W2105621451","https://openalex.org/W2140036815","https://openalex.org/W2145071835","https://openalex.org/W2482567233","https://openalex.org/W2489487449","https://openalex.org/W2496297199","https://openalex.org/W2509654948","https://openalex.org/W2514580099","https://openalex.org/W2564063843","https://openalex.org/W2613228905","https://openalex.org/W2615497679","https://openalex.org/W2743064457","https://openalex.org/W2774624432","https://openalex.org/W2883271562","https://openalex.org/W2890410227","https://openalex.org/W2894982503","https://openalex.org/W2897356390","https://openalex.org/W2904064004","https://openalex.org/W2912404939","https://openalex.org/W2930957955","https://openalex.org/W2950493386","https://openalex.org/W2952718163","https://openalex.org/W2970641574","https://openalex.org/W2971133212","https://openalex.org/W2987222756","https://openalex.org/W2996442464","https://openalex.org/W3003265726","https://openalex.org/W3007271522","https://openalex.org/W3011595896","https://openalex.org/W3012912574","https://openalex.org/W3035511822","https://openalex.org/W3036129238","https://openalex.org/W3043097943","https://openalex.org/W3081422235","https://openalex.org/W3081481921","https://openalex.org/W3093581739","https://openalex.org/W3100612294","https://openalex.org/W3101158499","https://openalex.org/W3155371331","https://openalex.org/W3167367835","https://openalex.org/W3169228325","https://openalex.org/W3172303411","https://openalex.org/W3174209494","https://openalex.org/W3174368915","https://openalex.org/W4211086402","https://openalex.org/W4212986650","https://openalex.org/W4221126228","https://openalex.org/W4285326639","https://openalex.org/W4289533980","https://openalex.org/W4289751787","https://openalex.org/W4290943423","https://openalex.org/W4306317674","https://openalex.org/W4318823771","https://openalex.org/W4365794597","https://openalex.org/W4367046899","https://openalex.org/W4367047417","https://openalex.org/W4379539320","https://openalex.org/W4382239895","https://openalex.org/W4384890876","https://openalex.org/W4385567900","https://openalex.org/W4385768247","https://openalex.org/W4388451578","https://openalex.org/W4396220739","https://openalex.org/W4396736136","https://openalex.org/W4403582823","https://openalex.org/W4406458149","https://openalex.org/W6600319451","https://openalex.org/W6601949647"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"The":[0,137,181],"problem":[1],"of":[2,30,49,58,70,107,183],"career":[3,51,62,128,142],"trajectory":[4,143],"prediction":[5],"(CTP)":[6],"aims":[7],"to":[8,66],"predict":[9],"one's":[10,166],"future":[11,167],"employer":[12],"or":[13],"job":[14,72,120],"position.":[15],"While":[16],"several":[17],"CTP":[18,162],"methods":[19,32,163],"have":[20],"been":[21],"developed":[22],"for":[23,133],"this":[24],"problem,":[25],"we":[26,84,124],"posit":[27],"that":[28,92,146],"none":[29],"these":[31],"(1)":[33],"jointly":[34],"considers":[35],"the":[36,55,71,76,81,94,105,117],"mutual":[37],"ternary":[38],"dependency":[39],"between":[40],"three":[41],"key":[42,59],"units":[43,60],"(i.e.,":[44],"user,":[45],"position,":[46],"and":[47,52,149,159,169,175],"company)":[48],"a":[50,86,108,134,140],"(2)":[53],"captures":[54],"characteristic":[56],"shifts":[57],"in":[61,75,119,164],"over":[63],"time,":[64],"leading":[65],"an":[67,126],"inaccurate":[68],"understanding":[69],"movement":[73,121],"patterns":[74],"labor":[77],"market.":[78],"To":[79],"address":[80],"above":[82],"challenges,":[83],"propose":[85],"novel":[87],"solution,":[88],"named":[89],"as":[90],"CAPER,":[91],"solves":[93],"challenges":[95],"via":[96],"sophisticated":[97],"temporal":[98],"knowledge":[99,110],"graph":[100],"(TKG)":[101],"modeling.":[102],"It":[103],"enables":[104],"utilization":[106],"graph-structured":[109],"base":[111],"with":[112],"rich":[113],"expressiveness,":[114],"effectively":[115],"preserving":[116],"changes":[118],"patterns.":[122],"Furthermore,":[123],"devise":[125],"extrapolated":[127],"reasoning":[129,157],"task":[130],"on":[131,139,171],"TKG":[132,156],"realistic":[135],"evaluation.":[136],"experiments":[138],"real-world":[141],"dataset":[144],"demonstrate":[145],"CAPER":[147,184],"consistently":[148],"significantly":[150],"outperforms":[151],"four":[152],"baselines,":[153],"two":[154],"recent":[155],"methods,":[158],"five":[160],"state-of-the-art":[161],"predicting":[165],"companies":[168],"positions--i.e.,":[170],"average,":[172],"yielding":[173],"6.80%":[174],"34.58%":[176],"more":[177],"accurate":[178],"predictions,":[179],"respectively.":[180],"codebase":[182],"is":[185],"available":[186],"at":[187],"https://github.com/Bigdasgit/CAPER.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}
