{"id":"https://openalex.org/W2911112412","doi":"https://doi.org/10.1109/ijcnn.2019.8852259","title":"Fusion Strategies for Learning User Embeddings with Neural Networks","display_name":"Fusion Strategies for Learning User Embeddings with Neural Networks","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2911112412","doi":"https://doi.org/10.1109/ijcnn.2019.8852259","mag":"2911112412"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852259","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1901.02322","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020380115","display_name":"Philipp Blandfort","orcid":"https://orcid.org/0000-0002-1516-3780"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]},{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Philipp Blandfort","raw_affiliation_strings":["Technische Universit\u00e4t Kaiserslautern, Germany","German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universit\u00e4t Kaiserslautern, Germany","institution_ids":["https://openalex.org/I153267046"]},{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029350364","display_name":"Tushar Karayil","orcid":null},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tushar Karayil","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029509133","display_name":"Federico Raue","orcid":"https://orcid.org/0000-0002-8604-6207"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Federico Raue","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany","German Research Centre for Artificial Intelligence"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany","institution_ids":["https://openalex.org/I33256026"]},{"raw_affiliation_string":"German Research Centre for Artificial Intelligence","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019899855","display_name":"J.J. van Hees","orcid":"https://orcid.org/0000-0002-0084-8998"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jorn Hees","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101904182","display_name":"Andreas Dengel","orcid":"https://orcid.org/0000-0002-6100-8255"},"institutions":[{"id":"https://openalex.org/I153267046","display_name":"University of Kaiserslautern","ror":"https://ror.org/04zrf7b53","country_code":"DE","type":"education","lineage":["https://openalex.org/I153267046"]},{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Dengel","raw_affiliation_strings":["Technische Universit\u00e4t Kaiserslautern, Germany","German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universit\u00e4t Kaiserslautern, Germany","institution_ids":["https://openalex.org/I153267046"]},{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany","institution_ids":["https://openalex.org/I33256026"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5020380115"],"corresponding_institution_ids":["https://openalex.org/I153267046","https://openalex.org/I33256026"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.00837949,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9965999722480774,"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":0.9965999722480774,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9944000244140625,"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":0.9930999875068665,"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/embedding","display_name":"Embedding","score":0.8612313270568848},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7468467950820923},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.664090096950531},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6370459794998169},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.6060082912445068},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5494463443756104},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.546840488910675},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.44893771409988403},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4119991362094879}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.8612313270568848},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7468467950820923},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.664090096950531},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6370459794998169},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.6060082912445068},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5494463443756104},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.546840488910675},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.44893771409988403},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4119991362094879},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","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":4,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852259","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852259","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1901.02322","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1901.02322","pdf_url":"https://arxiv.org/pdf/1901.02322","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":"","raw_type":"text"},{"id":"mag:2911112412","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1901.02322.pdf","pdf_url":null,"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":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1901.02322","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1901.02322","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1901.02322","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1901.02322","pdf_url":"https://arxiv.org/pdf/1901.02322","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":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2911112412.pdf","grobid_xml":"https://content.openalex.org/works/W2911112412.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W16794263","https://openalex.org/W66373487","https://openalex.org/W190019437","https://openalex.org/W427367409","https://openalex.org/W1521958187","https://openalex.org/W1983908352","https://openalex.org/W1994389483","https://openalex.org/W1999627569","https://openalex.org/W2035265584","https://openalex.org/W2089349245","https://openalex.org/W2131744502","https://openalex.org/W2141277304","https://openalex.org/W2160084280","https://openalex.org/W2162979096","https://openalex.org/W2184188583","https://openalex.org/W2200988052","https://openalex.org/W2219888463","https://openalex.org/W2251471769","https://openalex.org/W2295739661","https://openalex.org/W2402144811","https://openalex.org/W2596484740","https://openalex.org/W2611484930","https://openalex.org/W2657631929","https://openalex.org/W2754336679","https://openalex.org/W2899930077","https://openalex.org/W2953012738","https://openalex.org/W2962810352","https://openalex.org/W2963287333","https://openalex.org/W2963717374","https://openalex.org/W2964126051","https://openalex.org/W2964345214","https://openalex.org/W6607826596","https://openalex.org/W6614842995","https://openalex.org/W6631315095","https://openalex.org/W6679775712","https://openalex.org/W6686207219","https://openalex.org/W6691797780","https://openalex.org/W6713134421","https://openalex.org/W6718171111","https://openalex.org/W6736751715","https://openalex.org/W6743846187","https://openalex.org/W6756148483","https://openalex.org/W6917172014"],"related_works":["https://openalex.org/W2977902396","https://openalex.org/W3080903012","https://openalex.org/W3199449367","https://openalex.org/W2592801061","https://openalex.org/W2761790105","https://openalex.org/W2946206074","https://openalex.org/W3080236009","https://openalex.org/W2991822700","https://openalex.org/W3156968278","https://openalex.org/W2997432967","https://openalex.org/W3100591234","https://openalex.org/W3127699589","https://openalex.org/W2809897079","https://openalex.org/W1963973267","https://openalex.org/W2785864132","https://openalex.org/W3035566692","https://openalex.org/W3080720290","https://openalex.org/W2984580555","https://openalex.org/W2781515151","https://openalex.org/W1902312632"],"abstract_inverted_index":{"Growing":[0],"amounts":[1],"of":[2,15,94,160,184],"online":[3],"user":[4,16,52,58,69,79],"data":[5],"motivate":[6],"the":[7,45,92,97,114,140,153,186],"need":[8],"for":[9,26,39,63,67],"automated":[10],"processing":[11],"techniques.":[12],"In":[13,100],"case":[14],"ratings,":[17],"one":[18],"interesting":[19],"option":[20],"is":[21,89],"to":[22,28,49,53,189],"use":[23],"neural":[24,85,125],"networks":[25],"learning":[27],"predict":[29],"ratings":[30,109],"given":[31],"an":[32,42,105],"item":[33,77],"and":[34,78,87,164],"a":[35,54,56,133],"user.":[36],"While":[37],"training":[38],"prediction,":[40],"such":[41],"approach":[43],"at":[44],"same":[46],"time":[47],"learns":[48],"map":[50],"each":[51],"vector,":[55],"so-called":[57],"embedding.":[59],"Such":[60],"embeddings":[61,182],"can":[62,81],"example":[64],"be":[65,82,198],"valuable":[66],"estimating":[68],"similarity.":[70],"However,":[71],"there":[72],"are":[73,183],"various":[74],"ways":[75],"how":[76,91],"information":[80],"combined":[83],"in":[84,124,158],"networks,":[86],"it":[88],"unclear":[90],"way":[93],"combining":[95],"affects":[96,156],"resulting":[98],"embeddings.":[99],"this":[101],"paper,":[102],"we":[103,112,131,168],"run":[104],"experiment":[106],"on":[107,116,193],"movie":[108],"data,":[110],"where":[111],"analyze":[113],"effect":[115],"embedding":[117,129,148,165,176],"quality":[118],"caused":[119],"by":[120],"several":[121],"fusion":[122,154],"strategies":[123],"networks.":[126],"For":[127],"evaluating":[128],"quality,":[130],"propose":[132],"novel":[134],"measure,":[135],"Pair-Distance":[136],"Correlation,":[137],"which":[138],"quantifies":[139],"condition":[141],"that":[142,152,170,180],"similar":[143,147],"users":[144],"should":[145,197],"have":[146],"vectors.":[149],"We":[150],"find":[151,169],"strategy":[155],"results":[157],"terms":[159],"both":[161],"prediction":[162,171,195],"performance":[163,172],"quality.":[166,177],"Surprisingly,":[167],"not":[173],"necessarily":[174],"reflects":[175],"This":[178],"suggests":[179],"if":[181],"interest,":[185],"common":[187],"tendency":[188],"select":[190],"models":[191],"based":[192],"their":[194],"ability":[196],"reconsidered.":[199]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}