{"id":"https://openalex.org/W4306317331","doi":"https://doi.org/10.1145/3511808.3557441","title":"RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation","display_name":"RuDi: Explaining Behavior Sequence Models by Automatic Statistics Generation and Rule Distillation","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4306317331","doi":"https://doi.org/10.1145/3511808.3557441"},"language":"en","primary_location":{"id":"doi:10.1145/3511808.3557441","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557441","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","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/A5100447010","display_name":"Yao Zhang","orcid":"https://orcid.org/0000-0003-2789-6962"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yao Zhang","raw_affiliation_strings":["Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001877137","display_name":"Yun Xiong","orcid":"https://orcid.org/0000-0002-8575-5415"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Xiong","raw_affiliation_strings":["Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066302581","display_name":"Yiheng Sun","orcid":"https://orcid.org/0000-0002-3192-2281"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiheng Sun","raw_affiliation_strings":["Tencent Weixin Group, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Weixin Group, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043558899","display_name":"Caihua Shan","orcid":"https://orcid.org/0000-0001-9012-7088"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Caihua Shan","raw_affiliation_strings":["Microsoft Research Asia, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia, Shanghai, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100365667","display_name":"Tian Lu","orcid":"https://orcid.org/0000-0003-3730-1897"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tian Lu","raw_affiliation_strings":["Arizona State University, Tempe, China"],"affiliations":[{"raw_affiliation_string":"Arizona State University, Tempe, China","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113478273","display_name":"Hui Song","orcid":"https://orcid.org/0000-0001-6804-3232"},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Song","raw_affiliation_strings":["Tencent Weixin Group, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Tencent Weixin Group, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020370754","display_name":"Yangyong Zhu","orcid":"https://orcid.org/0000-0002-6258-0747"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yangyong Zhu","raw_affiliation_strings":["Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5100447010"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.5197,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.63806292,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2651","last_page":"2660"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9948999881744385,"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"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9948999881744385,"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"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9940000176429749,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10028","display_name":"Topic Modeling","score":0.9898999929428101,"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/computer-science","display_name":"Computer science","score":0.587286114692688},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.5776047110557556},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5200314521789551},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4267078936100006},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.33305823802948},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15697750449180603},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.10645896196365356},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.05903679132461548}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.587286114692688},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.5776047110557556},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5200314521789551},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4267078936100006},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.33305823802948},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15697750449180603},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.10645896196365356},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.05903679132461548},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3511808.3557441","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3511808.3557441","pdf_url":null,"source":{"id":"https://openalex.org/S4363608762","display_name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","id":"https://metadata.un.org/sdg/2","score":0.5}],"awards":[{"id":"https://openalex.org/G2520395425","display_name":null,"funder_award_id":"2022M710747","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G5340839507","display_name":null,"funder_award_id":"U1936213","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1552061030","https://openalex.org/W1565746575","https://openalex.org/W1625390266","https://openalex.org/W2000412498","https://openalex.org/W2063978378","https://openalex.org/W2126316555","https://openalex.org/W2182353144","https://openalex.org/W2584335703","https://openalex.org/W2584781382","https://openalex.org/W2804966426","https://openalex.org/W2902237356","https://openalex.org/W2964306570","https://openalex.org/W2991044292","https://openalex.org/W2997668353","https://openalex.org/W3008932301","https://openalex.org/W3034368386","https://openalex.org/W3090007645","https://openalex.org/W3091533194","https://openalex.org/W3093545390","https://openalex.org/W3138154797","https://openalex.org/W4289097366","https://openalex.org/W4389138872"],"related_works":["https://openalex.org/W2033987689","https://openalex.org/W1770217717","https://openalex.org/W4288828925","https://openalex.org/W2947372211","https://openalex.org/W1553039714","https://openalex.org/W2386487727","https://openalex.org/W2468314138","https://openalex.org/W143838428","https://openalex.org/W1562846600","https://openalex.org/W2361035979"],"abstract_inverted_index":{"Risk":[0],"scoring":[1],"systems":[2,47],"have":[3,29],"been":[4],"widely":[5],"deployed":[6],"in":[7,51,135],"many":[8,22],"applications,":[9],"which":[10],"assign":[11,78],"risk":[12],"scores":[13],"to":[14,17,40,64,80,171],"users":[15],"according":[16],"their":[18,37],"behavior":[19,70],"sequences.":[20],"Though":[21],"deep":[23],"learning":[24],"methods":[25],"with":[26,146],"sophisticated":[27],"designs":[28],"achieved":[30],"promising":[31],"results,":[32],"the":[33,88,107,136,154],"black-box":[34,93,110],"nature":[35],"hinders":[36],"applications":[38],"due":[39],"fairness,":[41],"explainability,":[42],"and":[43,77,95,167],"compliance":[44],"consideration.":[45],"Rule-based":[46],"are":[48,141],"considered":[49],"reliable":[50],"these":[52],"sensitive":[53],"scenarios.":[54],"However,":[55],"building":[56],"a":[57,101,119,130],"rule":[58,97],"system":[59],"is":[60],"labor-intensive.":[61],"Experts":[62],"need":[63],"find":[65],"informative":[66,133],"statistics":[67,76,124,134,140],"from":[68],"user":[69],"sequences,":[71],"design":[72,118],"rules":[73,145],"based":[74],"on":[75,162],"weights":[79],"each":[81],"rule.":[82],"In":[83],"this":[84],"paper,":[85],"we":[86],"bridge":[87],"gap":[89],"between":[90],"effective":[91],"but":[92],"models":[94,112],"transparent":[96],"models.":[98,116,158],"We":[99,117,159],"propose":[100],"two-stage":[102],"method,":[103],"RuDi,":[104],"that":[105,127],"distills":[106],"knowledge":[108],"of":[109,132,156],"teacher":[111,157],"into":[113,143],"rule-based":[114],"student":[115],"Monte":[120],"Carlo":[121],"tree":[122],"search-based":[123],"generation":[125],"method":[126],"can":[128],"provide":[129],"set":[131],"first":[137],"stage.":[138],"Then":[139],"composed":[142],"logical":[144,150],"our":[147],"proposed":[148],"neural":[149],"networks":[151],"by":[152],"mimicking":[153],"outputs":[155],"evaluate":[160],"RuDi":[161],"three":[163],"real-world":[164],"public":[165],"datasets":[166],"an":[168],"industrial":[169],"dataset":[170],"demonstrate":[172],"its":[173],"effectiveness.":[174]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}