{"id":"https://openalex.org/W4400267074","doi":"https://doi.org/10.1145/3637528.3671759","title":"Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning","display_name":"Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4400267074","doi":"https://doi.org/10.1145/3637528.3671759"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3671759","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671759","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2407.00082","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014939822","display_name":"Xiao Han","orcid":"https://orcid.org/0000-0002-3478-964X"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiao Han","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0002-3478-964X","affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100768322","display_name":"Chen Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Zhu","raw_affiliation_strings":["Career Science Lab, BOSS Zhipin, University of Science and Technology of China, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4817-482X","affiliations":[{"raw_affiliation_string":"Career Science Lab, BOSS Zhipin, University of Science and Technology of China, Beijing, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100674751","display_name":"Xiao Hu","orcid":"https://orcid.org/0009-0002-6761-6259"},"institutions":[{"id":"https://openalex.org/I4210157345","display_name":"Venus Medtech (China)","ror":"https://ror.org/05xzt2h26","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210157345"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Hu","raw_affiliation_strings":["Career Science Lab, BOSS Zhipin, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0002-6761-6259","affiliations":[{"raw_affiliation_string":"Career Science Lab, BOSS Zhipin, Beijing, China","institution_ids":["https://openalex.org/I4210157345"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102883290","display_name":"Chuan Qin","orcid":"https://orcid.org/0000-0002-5354-8630"},"institutions":[{"id":"https://openalex.org/I4210157345","display_name":"Venus Medtech (China)","ror":"https://ror.org/05xzt2h26","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210157345"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Qin","raw_affiliation_strings":["Career Science Lab, BOSS Zhipin, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5354-8630","affiliations":[{"raw_affiliation_string":"Career Science Lab, BOSS Zhipin, Beijing, China","institution_ids":["https://openalex.org/I4210157345"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100645854","display_name":"Xiangyu Zhao","orcid":"https://orcid.org/0000-0003-2926-4416"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xiangyu Zhao","raw_affiliation_strings":["City University of Hong Kong, Hong Kong, China"],"raw_orcid":"https://orcid.org/0000-0003-2926-4416","affiliations":[{"raw_affiliation_string":"City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049015446","display_name":"Hengshu Zhu","orcid":"https://orcid.org/0000-0003-4570-643X"},"institutions":[{"id":"https://openalex.org/I4210157345","display_name":"Venus Medtech (China)","ror":"https://ror.org/05xzt2h26","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210157345"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hengshu Zhu","raw_affiliation_strings":["Career Science Lab, BOSS Zhipin, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4570-643X","affiliations":[{"raw_affiliation_string":"Career Science Lab, BOSS Zhipin, Beijing, China","institution_ids":["https://openalex.org/I4210157345"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1004","last_page":"1015"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9944000244140625,"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/T11409","display_name":"Advanced Wireless Network Optimization","score":0.9735999703407288,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7771623134613037},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.548378050327301},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4947020709514618},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.46667730808258057},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.46306151151657104},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4566373825073242},{"id":"https://openalex.org/keywords/session","display_name":"Session (web analytics)","score":0.43916216492652893},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4324074387550354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4314594864845276},{"id":"https://openalex.org/keywords/semantic-matching","display_name":"Semantic matching","score":0.4117637574672699},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.34929990768432617},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.17398032546043396}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7771623134613037},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.548378050327301},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4947020709514618},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.46667730808258057},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.46306151151657104},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4566373825073242},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.43916216492652893},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4324074387550354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4314594864845276},{"id":"https://openalex.org/C2778493491","wikidata":"https://www.wikidata.org/wiki/Q7449072","display_name":"Semantic matching","level":3,"score":0.4117637574672699},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.34929990768432617},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.17398032546043396},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3637528.3671759","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3637528.3671759","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2407.00082","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.00082","pdf_url":"https://arxiv.org/pdf/2407.00082","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2407.00082","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2407.00082","pdf_url":"https://arxiv.org/pdf/2407.00082","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1805381866","https://openalex.org/W1982235053","https://openalex.org/W1987431925","https://openalex.org/W2112008792","https://openalex.org/W2117311203","https://openalex.org/W2119541122","https://openalex.org/W2150886314","https://openalex.org/W2196920274","https://openalex.org/W2253995343","https://openalex.org/W2461865494","https://openalex.org/W2788295351","https://openalex.org/W2796608345","https://openalex.org/W2809307135","https://openalex.org/W2912745432","https://openalex.org/W2945827670","https://openalex.org/W2963085847","https://openalex.org/W2963868026","https://openalex.org/W2971133212","https://openalex.org/W2980540750","https://openalex.org/W2984100107","https://openalex.org/W2998431760","https://openalex.org/W2998677531","https://openalex.org/W3035666843","https://openalex.org/W3041105438","https://openalex.org/W3045200674","https://openalex.org/W3094605801","https://openalex.org/W3098400049","https://openalex.org/W3100278010","https://openalex.org/W3101708421","https://openalex.org/W3102778384","https://openalex.org/W3133849783","https://openalex.org/W3153325943","https://openalex.org/W3165913101","https://openalex.org/W3166215705","https://openalex.org/W3166914430","https://openalex.org/W3173365306","https://openalex.org/W3178328486","https://openalex.org/W3188604155","https://openalex.org/W3208338073","https://openalex.org/W4220909642","https://openalex.org/W4224321885","https://openalex.org/W4225412853","https://openalex.org/W4283702870","https://openalex.org/W4290878067","https://openalex.org/W4306316988","https://openalex.org/W4319653664","https://openalex.org/W4324311885","https://openalex.org/W4362454650","https://openalex.org/W4367046946","https://openalex.org/W4382240261","https://openalex.org/W4385567536","https://openalex.org/W4385568060","https://openalex.org/W4387507359","https://openalex.org/W4387846486","https://openalex.org/W4393056885","https://openalex.org/W4393147839","https://openalex.org/W4396712781"],"related_works":["https://openalex.org/W4230197055","https://openalex.org/W1657011257","https://openalex.org/W4236298560","https://openalex.org/W4322508012","https://openalex.org/W4394832455","https://openalex.org/W3123407378","https://openalex.org/W4236362086","https://openalex.org/W1986106996","https://openalex.org/W4285012873","https://openalex.org/W2122517733"],"abstract_inverted_index":{"Job":[0],"recommender":[1],"systems":[2],"are":[3],"crucial":[4],"for":[5,168],"aligning":[6],"job":[7,20,32,87,94,129],"opportunities":[8,25],"with":[9],"job-seekers":[10],"in":[11,138,191,197],"online":[12,192],"job-seeking.":[13],"However,":[14],"users":[15],"tend":[16],"to":[17,22,43,61,110,125,153,164],"adjust":[18],"their":[19],"preferences":[21],"secure":[23],"employment":[24],"continually,":[26],"which":[27],"limits":[28],"the":[29,99,127,134,181,205],"performance":[30],"of":[31,37,69,78,114,136,184,209],"recommendations.":[33],"The":[34,211],"inherent":[35],"frequency":[36],"preference":[38,65,88,130,143],"drift":[39],"poses":[40],"a":[41,56,120,158],"challenge":[42],"promptly":[44],"and":[45,71,90,105,207],"precisely":[46],"capture":[47,126],"user":[48,64,100],"preferences.":[49,171],"To":[50,132],"address":[51],"this":[52],"issue,":[53],"we":[54,118,145],"propose":[55,147],"novel":[57],"session-based":[58,107,166],"framework,":[59],"BISTRO,":[60],"timely":[62],"model":[63],"through":[66],"fusion":[67],"learning":[68,123],"semantic":[70,83,108],"behavioral":[72],"information.":[73],"Specifically,":[74],"BISTRO":[75,97,188],"is":[76,162,214],"composed":[77],"three":[79,175],"stages:":[80],"1)":[81],"coarse-grained":[82],"clustering,":[84],"2)":[85],"fine-grained":[86],"extraction,":[89],"3)":[91],"personalized":[92,170],"top-k":[93],"recommendation.":[95],"Initially,":[96],"segments":[98],"interaction":[101,167],"sequence":[102],"into":[103],"sessions":[104],"leverages":[106],"clustering":[109],"achieve":[111],"broad":[112],"identification":[113],"person-job":[115],"matching.":[116],"Subsequently,":[117],"design":[119],"hypergraph":[121],"wavelet":[122,150],"method":[124],"nuanced":[128],"drift.":[131],"mitigate":[133],"effect":[135],"noise":[137],"interactions":[139],"caused":[140],"by":[141],"frequent":[142],"drift,":[144],"innovatively":[146],"an":[148],"adaptive":[149],"filtering":[151],"technique":[152],"remove":[154],"noisy":[155],"interaction.":[156],"Finally,":[157],"recurrent":[159],"neural":[160],"network":[161],"utilized":[163],"analyze":[165],"inferring":[169],"Extensive":[172],"experiments":[173],"on":[174],"real-world":[176],"offline":[177],"recruitment":[178,199],"datasets":[179],"demonstrate":[180],"significant":[182],"performances":[183],"our":[185],"framework.":[186],"Significantly,":[187],"also":[189],"excels":[190],"experiments,":[193],"affirming":[194],"its":[195],"effectiveness":[196],"live":[198],"settings.":[200],"This":[201],"dual":[202],"success":[203],"underscores":[204],"robustness":[206],"adaptability":[208],"BISTRO.":[210],"source":[212],"code":[213],"available":[215],"at":[216],"https://github.com/Applied-Machine-Learning-Lab/BISTRO.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2024-07-03T00:00:00"}