{"id":"https://openalex.org/W3035669589","doi":"https://doi.org/10.1145/3397271.3401445","title":"Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network","display_name":"Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network","publication_year":2020,"publication_date":"2020-07-25","ids":{"openalex":"https://openalex.org/W3035669589","doi":"https://doi.org/10.1145/3397271.3401445","mag":"3035669589"},"language":"en","primary_location":{"id":"doi:10.1145/3397271.3401445","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401445","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2110.04002","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Lianghao Xia","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lianghao Xia","raw_affiliation_strings":["South China University of Technology, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"South China University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chao Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chao Huang","raw_affiliation_strings":["JD Finance America Corporation, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"JD Finance America Corporation, Mountain View, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yong Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Xu","raw_affiliation_strings":["South China University of Technology & Peng Cheng Laboratory, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"South China University of Technology & Peng Cheng Laboratory, Guangzhou, China","institution_ids":["https://openalex.org/I4210136793","https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Peng Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng Dai","raw_affiliation_strings":["JD Finance America Corporation, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"JD Finance America Corporation, Mountain View, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bo Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["Boshi Qiangzhi Science and Technology Co., Ltd, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"Boshi Qiangzhi Science and Technology Co., Ltd, Shenzhen, China","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Liefeng Bo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liefeng Bo","raw_affiliation_strings":["JD Finance America Corporation, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"JD Finance America Corporation, Mountain View, CA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":16.4102,"has_fulltext":false,"cited_by_count":139,"citation_normalized_percentile":{"value":0.99088717,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2397","last_page":"2406"},"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/T10028","display_name":"Topic Modeling","score":0.9901999831199646,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9779999852180481,"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/recommender-system","display_name":"Recommender system","score":0.5540000200271606},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.46209999918937683},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.44679999351501465},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.44440001249313354},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.4041999876499176},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.40139999985694885},{"id":"https://openalex.org/keywords/interactor","display_name":"Interactor","score":0.3840000033378601},{"id":"https://openalex.org/keywords/multiplex","display_name":"Multiplex","score":0.3774000108242035},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.35359999537467957}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7684999704360962},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.5540000200271606},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.46209999918937683},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4528000056743622},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.44679999351501465},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.44440001249313354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42890000343322754},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.4041999876499176},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.40139999985694885},{"id":"https://openalex.org/C166998942","wikidata":"https://www.wikidata.org/wiki/Q16969238","display_name":"Interactor","level":2,"score":0.3840000033378601},{"id":"https://openalex.org/C2781188995","wikidata":"https://www.wikidata.org/wiki/Q6934982","display_name":"Multiplex","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.36899998784065247},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3222000002861023},{"id":"https://openalex.org/C78639753","wikidata":"https://www.wikidata.org/wiki/Q3318160","display_name":"Behavioral modeling","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.26829999685287476},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.26409998536109924},{"id":"https://openalex.org/C2989277270","wikidata":"https://www.wikidata.org/wiki/Q168338","display_name":"Behavioral analysis","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C5570062","wikidata":"https://www.wikidata.org/wiki/Q3919817","display_name":"Behavioural sciences","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.25099998712539673}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3397271.3401445","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3397271.3401445","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2110.04002","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.04002","pdf_url":"https://arxiv.org/pdf/2110.04002","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:hub.hku.hk:10722/308822","is_oa":false,"landing_page_url":"https://hub.hku.hk/handle/10722/308822","pdf_url":null,"source":{"id":"https://openalex.org/S4377196271","display_name":"The HKU Scholars Hub (University of Hong Kong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I889458895","host_organization_name":"University of Hong Kong","host_organization_lineage":["https://openalex.org/I889458895"],"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":"Conference_Paper"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2110.04002","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.04002","pdf_url":"https://arxiv.org/pdf/2110.04002","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1720514416","https://openalex.org/W2026773017","https://openalex.org/W2054141820","https://openalex.org/W2137245235","https://openalex.org/W2253995343","https://openalex.org/W2471920251","https://openalex.org/W2472158773","https://openalex.org/W2575006718","https://openalex.org/W2723293840","https://openalex.org/W2740920897","https://openalex.org/W2783272285","https://openalex.org/W2783944588","https://openalex.org/W2907754626","https://openalex.org/W2907827821","https://openalex.org/W2908404712","https://openalex.org/W2911575572","https://openalex.org/W2912057614","https://openalex.org/W2914721378","https://openalex.org/W2945266622","https://openalex.org/W2945623882","https://openalex.org/W2945827670","https://openalex.org/W2947766753","https://openalex.org/W2951045934","https://openalex.org/W2951369132","https://openalex.org/W2951570486","https://openalex.org/W2953968544","https://openalex.org/W2962992837","https://openalex.org/W2963911286","https://openalex.org/W2984100107","https://openalex.org/W2984582982","https://openalex.org/W2997878072","https://openalex.org/W6629340653"],"related_works":[],"abstract_inverted_index":{"Capturing":[0],"users'":[1],"precise":[2],"preferences":[3],"is":[4,20,66,175,200],"of":[5,23,36,50,59,83,97,103,132,165,184,187],"great":[6],"importance":[7],"in":[8,79,140,217],"various":[9],"recommender":[10],"systems":[11],"(e.g.,":[12,71],"e-commerce":[13],"platforms":[14],"and":[15,47,75,77,129,136,212,226],"online":[16],"advertising":[17],"sites),":[18],"which":[19,99],"the":[21,90,101,109,122,159,166,181,190,204],"basis":[22],"how":[24],"to":[25,31,42,120,157,177,202],"present":[26],"personalized":[27],"interesting":[28],"product":[29],"lists":[30],"individual":[32],"users.":[33],"In":[34,145],"spite":[35],"significant":[37,234],"progress":[38],"has":[39],"been":[40],"made":[41],"consider":[43],"relations":[44,86],"between":[45],"users":[46],"items,":[48],"most":[49],"existing":[51],"recommendation":[52,105,123],"techniques":[53],"solely":[54],"focus":[55],"on":[56,222],"singular":[57],"type":[58],"user-item":[60,63],"interactions.":[61],"However,":[62],"interactive":[64],"behavior":[65,85,138,168,188,210,231],"often":[67],"exhibited":[68],"with":[69,124],"multi-type":[70],"page":[72],"view,":[73],"add-to-favorite":[74],"purchase)":[76],"inter-dependent":[78],"nature.":[80],"The":[81],"overlook":[82],"multiplex":[84,125],"can":[87],"hardly":[88],"recognize":[89],"multi-modal":[91],"contextual":[92,182],"signals":[93,183],"across":[94,207],"different":[95,185],"types":[96,186],"interactions,":[98],"limit":[100],"feasibility":[102],"current":[104],"methods.":[106],"To":[107],"tackle":[108],"above":[110],"challenge,":[111],"this":[112],"work":[113],"proposes":[114],"a":[115,141,152,171,196,227],"Memory-Augmented":[116],"Transformer":[117],"Networks":[118],"(MATN),":[119],"enable":[121],"behavioral":[126,134],"relational":[127],"information,":[128],"joint":[130],"modeling":[131],"type-specific":[133],"context":[135],"type-wise":[137],"inter-dependencies,":[139],"fully":[142],"automatic":[143],"manner.":[144],"our":[146],"MATN":[147,179,238],"framework,":[148],"we":[149],"first":[150],"develop":[151],"transformer-based":[153],"multi-behavior":[154],"relation":[155],"encoder,":[156],"make":[158],"learned":[160],"interaction":[161,209],"representations":[162],"be":[163],"reflective":[164],"cross-type":[167],"relations.":[169],"Furthermore,":[170],"memory":[172],"attention":[173],"network":[174],"proposed":[176],"supercharge":[178],"capturing":[180],"into":[189],"category-specific":[191],"latent":[192],"embedding":[193],"space.":[194],"Finally,":[195],"cross-behavior":[197],"aggregation":[198],"component":[199],"introduced":[201],"promote":[203],"comprehensive":[205],"collaboration":[206],"type-aware":[208],"representations,":[211],"discriminate":[213],"their":[214],"inherent":[215],"contributions":[216],"assisting":[218],"recommendations.":[219],"Extensive":[220],"experiments":[221],"two":[223],"benchmark":[224],"datasets":[225],"real-world":[228],"e-commence":[229],"user":[230],"data":[232],"demonstrate":[233],"improvements":[235],"obtained":[236],"by":[237],"over":[239],"baselines.":[240],"Codes":[241],"are":[242],"available":[243],"at:":[244],"https://github.com/akaxlh/MATN.":[245]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":42},{"year":2024,"cited_by_count":36},{"year":2023,"cited_by_count":32},{"year":2022,"cited_by_count":16},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2020-06-19T00:00:00"}