{"id":"https://openalex.org/W2950260856","doi":"https://doi.org/10.1145/3292500.3330707","title":"Real-time Attention Based Look-alike Model for Recommender System","display_name":"Real-time Attention Based Look-alike Model for Recommender System","publication_year":2019,"publication_date":"2019-07-25","ids":{"openalex":"https://openalex.org/W2950260856","doi":"https://doi.org/10.1145/3292500.3330707","mag":"2950260856"},"language":"en","primary_location":{"id":"doi:10.1145/3292500.3330707","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330707","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1906.05022","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003892229","display_name":"Yudan Liu","orcid":"https://orcid.org/0000-0002-7496-7033"},"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":true,"raw_author_name":"Yudan Liu","raw_affiliation_strings":["WeiXin Group, Tencent Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"WeiXin Group, Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025388866","display_name":"Kaikai Ge","orcid":"https://orcid.org/0009-0006-0554-0761"},"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":"Kaikai Ge","raw_affiliation_strings":["WeiXin Group, Tencent Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"WeiXin Group, Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100437332","display_name":"Xu Zhang","orcid":"https://orcid.org/0009-0006-5685-316X"},"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":"Xu Zhang","raw_affiliation_strings":["WeiXin Group, Tencent Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"WeiXin Group, Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023086553","display_name":"Leyu Lin","orcid":"https://orcid.org/0000-0001-5471-500X"},"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":"Leyu Lin","raw_affiliation_strings":["WeiXin Group, Tencent Inc., Beijing, China"],"affiliations":[{"raw_affiliation_string":"WeiXin Group, Tencent Inc., Beijing, China","institution_ids":["https://openalex.org/I2250653659"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5003892229"],"corresponding_institution_ids":["https://openalex.org/I2250653659"],"apc_list":null,"apc_paid":null,"fwci":7.0301,"has_fulltext":false,"cited_by_count":45,"citation_normalized_percentile":{"value":0.9700684,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2765","last_page":"2773"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.986299991607666,"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":0.8468302488327026},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.7532345056533813},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6104922294616699},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6072574853897095},{"id":"https://openalex.org/keywords/concatenation","display_name":"Concatenation (mathematics)","score":0.580518364906311},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5125439167022705},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5024700164794922},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.481679767370224},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.46220919489860535},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4392213523387909},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4182482957839966},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.18360662460327148}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8468302488327026},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.7532345056533813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6104922294616699},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6072574853897095},{"id":"https://openalex.org/C87619178","wikidata":"https://www.wikidata.org/wiki/Q126002","display_name":"Concatenation (mathematics)","level":2,"score":0.580518364906311},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5125439167022705},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5024700164794922},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.481679767370224},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.46220919489860535},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4392213523387909},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4182482957839966},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.18360662460327148},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"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/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3292500.3330707","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3292500.3330707","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1906.05022","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1906.05022","pdf_url":"https://arxiv.org/pdf/1906.05022","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1906.05022","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1906.05022","pdf_url":"https://arxiv.org/pdf/1906.05022","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":20,"referenced_works":["https://openalex.org/W1530276735","https://openalex.org/W1585811901","https://openalex.org/W1977556410","https://openalex.org/W1993971593","https://openalex.org/W2027858833","https://openalex.org/W2042281163","https://openalex.org/W2103088651","https://openalex.org/W2133564696","https://openalex.org/W2153579005","https://openalex.org/W2295739661","https://openalex.org/W2475334473","https://openalex.org/W2511448335","https://openalex.org/W2512971201","https://openalex.org/W2586801569","https://openalex.org/W2597655663","https://openalex.org/W2723293840","https://openalex.org/W2741249238","https://openalex.org/W2963403868","https://openalex.org/W4294170691","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W2373577936","https://openalex.org/W3095575180","https://openalex.org/W2389596151","https://openalex.org/W4221148444","https://openalex.org/W4226054107","https://openalex.org/W4387678054","https://openalex.org/W4306784355","https://openalex.org/W2784004155","https://openalex.org/W3048601286","https://openalex.org/W2965925734"],"abstract_inverted_index":{"Recently,":[0],"deep":[1],"learning":[2,151,154],"models":[3,86],"play":[4],"more":[5,7,34,36],"and":[6,35,60,107,130,143,152,201,208,257,281],"important":[8],"roles":[9],"in":[10,90,268,298],"contents":[11,32,41],"recommender":[12,97,120,299],"systems.":[13,300],"However,":[14],"although":[15],"the":[16,23,30,58,83,101,124,145,173,179,187,191,230,240,246,292],"performance":[17,258],"of":[18,49,62,100,104,126,182,194,233,242,273,283],"recommendations":[19],"is":[20,70,291],"greatly":[21],"improved,":[22],"\"Matthew":[24],"effect\"":[25],"becomes":[26],"increasingly":[27],"evident.":[28],"While":[29],"head":[31],"get":[33],"popular,":[37],"many":[38],"competitive":[39],"long-tail":[40,80],"are":[42,93],"difficult":[43],"to":[44,74,171,205,214,250,276],"achieve":[45],"timely":[46],"exposure":[47],"because":[48,99],"lacking":[50],"behavior":[51],"features.":[52],"This":[53,109],"issue":[54],"has":[55,264],"badly":[56],"impacted":[57],"quality":[59,79,282],"diversity":[61,280],"recommendations.":[63,284],"To":[64],"solve":[65],"this":[66,290],"problem,":[67],"look-alike":[68,85,116,135,153,261,295],"algorithm":[69],"a":[71,112,162,215],"good":[72],"choice":[73],"extend":[75],"audience":[76,136],"for":[77,96,119],"high":[78],"contents.":[81],"But":[82],"traditional":[84],"which":[87,122,176,226],"widely":[88],"used":[89],"online":[91],"advertising":[92],"not":[94,227],"suitable":[95],"systems":[98],"strict":[102],"requirement":[103],"both":[105],"real-time":[106,113,129,134,294],"effectiveness.":[108,131],"paper":[110],"introduces":[111],"attention":[114,168,199,203,234],"based":[115],"model":[117,296],"(RALM)":[118],"systems,":[121],"tackles":[123],"challenge":[125],"conflict":[127],"between":[128],"RALM":[132,253,263],"realizes":[133],"extension":[137],"benefiting":[138],"from":[139],"seeds-to-user":[140],"similarity":[141],"prediction":[142,236],"improves":[144,178],"effectiveness":[146,256],"through":[147],"optimizing":[148],"user":[149,157],"representation":[150,158,211],"modeling.":[155],"For":[156],"learning,":[159],"we":[160,196,221,288],"propose":[161],"novel":[163],"neural":[164],"network":[165],"structure":[166],"named":[167],"merge":[169],"layer":[170],"replace":[172],"concatenation":[174],"layer,":[175],"significantly":[177],"expressive":[180],"ability":[181],"multi-fields":[183],"feature":[184],"learning.":[185],"On":[186],"other":[188],"hand,":[189],"considering":[190],"various":[192],"members":[193],"seeds,":[195],"design":[197],"global":[198],"unit":[200,204],"local":[202],"learn":[206],"robust":[207],"adaptive":[209],"seeds":[210,223,243],"with":[212],"respect":[213],"certain":[216],"target":[217],"user.":[218],"At":[219],"last,":[220],"introduce":[222],"clustering":[224],"mechanism":[225],"only":[228],"reduces":[229],"time":[231],"complexity":[232],"units":[235],"but":[237],"also":[238],"minimizes":[239],"loss":[241],"information":[244],"at":[245],"same":[247],"time.":[248],"According":[249],"our":[251],"experiments,":[252],"shows":[254],"superior":[255],"than":[259],"popular":[260],"models.":[262],"been":[265],"successfully":[266],"deployed":[267],"\"Top":[269],"Stories\"":[270],"Recommender":[271],"System":[272],"WeChat,":[274],"leading":[275],"great":[277],"improvement":[278],"on":[279],"As":[285],"far":[286],"as":[287],"know,":[289],"first":[293],"applied":[297]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":7}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}