{"id":"https://openalex.org/W4306705678","doi":"https://doi.org/10.1145/3551349.3559496","title":"MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation","display_name":"MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4306705678","doi":"https://doi.org/10.1145/3551349.3559496"},"language":"en","primary_location":{"id":"doi:10.1145/3551349.3559496","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3551349.3559496","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","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/2210.07660","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101589508","display_name":"Ge Fan","orcid":"https://orcid.org/0000-0001-5653-1626"},"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":"Ge Fan","raw_affiliation_strings":["Tencent Inc., China"],"raw_orcid":"https://orcid.org/0000-0001-5653-1626","affiliations":[{"raw_affiliation_string":"Tencent Inc., China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112911036","display_name":"Chaoyun Zhang","orcid":null},"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":"Chaoyun Zhang","raw_affiliation_strings":["Microsoft Research, China"],"raw_orcid":"https://orcid.org/0000-0002-1139-8654","affiliations":[{"raw_affiliation_string":"Microsoft Research, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100436977","display_name":"Kai Wang","orcid":"https://orcid.org/0000-0002-0788-561X"},"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":"Kai Wang","raw_affiliation_strings":["Tencent Inc., China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent Inc., China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045191293","display_name":"Junyang Chen","orcid":"https://orcid.org/0000-0002-1139-8654"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junyang Chen","raw_affiliation_strings":["the College of Computer Science and Software Engineering, Shenzhen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"the College of Computer Science and Software Engineering, Shenzhen University, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9962999820709229,"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/T11478","display_name":"Caching and Content Delivery","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.8434487581253052},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.6752428412437439},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5984382629394531},{"id":"https://openalex.org/keywords/collaborative-filtering","display_name":"Collaborative filtering","score":0.47891342639923096},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4294929504394531},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.42109981179237366},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39916783571243286},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39886346459388733},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3230081796646118}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8434487581253052},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.6752428412437439},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5984382629394531},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.47891342639923096},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4294929504394531},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.42109981179237366},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39916783571243286},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39886346459388733},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3230081796646118},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"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:10.1145/3551349.3559496","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3551349.3559496","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2210.07660","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.07660","pdf_url":"https://arxiv.org/pdf/2210.07660","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:2210.07660","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.07660","pdf_url":"https://arxiv.org/pdf/2210.07660","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":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6000000238418579}],"awards":[{"id":"https://openalex.org/G1022776785","display_name":null,"funder_award_id":"2022A1515011474","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"},{"id":"https://openalex.org/G2935348012","display_name":null,"funder_award_id":"62102265","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/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4306705678.pdf","grobid_xml":"https://content.openalex.org/works/W4306705678.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W2074694452","https://openalex.org/W2114079787","https://openalex.org/W2136189984","https://openalex.org/W2194775991","https://openalex.org/W2512971201","https://openalex.org/W2612388534","https://openalex.org/W2723293840","https://openalex.org/W2898085636","https://openalex.org/W2972801466","https://openalex.org/W2998702515","https://openalex.org/W3034345128","https://openalex.org/W3036320503","https://openalex.org/W3088381833","https://openalex.org/W3098468692","https://openalex.org/W3101704389","https://openalex.org/W3199222860","https://openalex.org/W3200275476","https://openalex.org/W4200132161","https://openalex.org/W4210827199","https://openalex.org/W4289533784","https://openalex.org/W4292216049","https://openalex.org/W4294170691","https://openalex.org/W4385245566"],"related_works":["https://openalex.org/W2772628444","https://openalex.org/W1484355083","https://openalex.org/W4220714703","https://openalex.org/W2098758514","https://openalex.org/W2735929803","https://openalex.org/W3008845055","https://openalex.org/W2170391450","https://openalex.org/W4376854386","https://openalex.org/W2202724490","https://openalex.org/W2508671622"],"abstract_inverted_index":{"Industrial":[0],"recommender":[1,45],"systems":[2],"usually":[3],"employ":[4],"multi-source":[5],"data":[6,18],"to":[7],"improve":[8,165],"the":[9,41,72,76,86,90,94,106,133,140,145,160,166],"recommendation":[10],"quality,":[11],"while":[12,58],"effectively":[13,59],"sharing":[14,70],"information":[15],"between":[16,62],"different":[17,63,103],"sources":[19],"remain":[20],"a":[21,28,67,150],"challenge.":[22],"In":[23],"this":[24],"paper,":[25],"we":[26],"introduce":[27],"novel":[29],"Multi-View":[30],"Approach":[31],"with":[32,117],"Hybrid":[33],"Attentive":[34],"Networks":[35],"(MV-HAN)":[36],"for":[37],"contents":[38,101,116],"retrieval":[39,77,113,142],"at":[40],"matching":[42,152],"stage":[43],"of":[44,102,114,168],"systems.":[46],"The":[47,82],"proposed":[48,134,161],"model":[49],"enables":[50,111],"high-order":[51],"feature":[52],"interaction":[53],"from":[54,93],"various":[55],"input":[56],"features":[57,108],"transferring":[60],"knowledge":[61],"types.":[64,81],"By":[65],"employing":[66],"well-placed":[68],"parameters":[69],"strategy,":[71],"MV-HAN":[73,84,135,146],"substantially":[74],"improves":[75],"performance":[78],"in":[79,89,149],"sparse":[80],"designed":[83],"inherits":[85],"efficiency":[87],"advantages":[88],"online":[91],"service":[92],"two-tower":[95],"model,":[96],"by":[97],"mapping":[98],"users":[99],"and":[100],"types":[104],"into":[105],"same":[107],"space.":[109],"This":[110],"fast":[112],"similar":[115],"an":[118],"approximate":[119],"nearest":[120],"neighbor":[121],"algorithm.":[122],"We":[123],"conduct":[124],"offline":[125],"experiments":[126],"on":[127,139],"several":[128],"industrial":[129],"datasets,":[130],"demonstrating":[131],"that":[132,159],"significantly":[136,164],"outperforms":[137],"baselines":[138],"content":[141],"tasks.":[143],"Importantly,":[144],"is":[147],"deployed":[148],"real-world":[151],"system.":[153],"Online":[154],"A/B":[155],"test":[156],"results":[157],"show":[158],"method":[162],"can":[163],"quality":[167],"recommendations.":[169]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}