{"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":"conference-paper","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/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":"Philipp Blandfort","raw_affiliation_strings":["Technische Universit\u00e4t Kaiserslautern, Germany","German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany"],"raw_orcid":null,"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"],"raw_orcid":null,"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"],"raw_orcid":null,"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"],"raw_orcid":null,"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"],"raw_orcid":null,"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":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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":"2331-8422","issn":["2331-8422"],"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":"2331-8422","issn":["2331-8422"],"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":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"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":"2331-8422","issn":["2331-8422"],"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":[2,15,94,159,183],"online":[3],"user":[4,16,52,58,69,79],"data":[5],"motivate":[6],"the":[7,45,92,97,113,139,152,185],"need":[8],"for":[9,26,39,63,67],"automated":[10],"processing":[11],"techniques.":[12],"In":[13],"case":[14],"ratings,":[17],"one":[18],"interesting":[19],"option":[20],"is":[21,89],"to":[22,28,49,53,188],"use":[23],"neural":[24,85,124],"networks":[25],"learning":[27],"predict":[29],"ratings":[30,108],"given":[31],"an":[32,42,104],"item":[33,77],"and":[34,78,87,163],"a":[35,54,56,132],"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":[61,181],"can":[62,81],"example":[64],"be":[65,82,197],"valuable":[66],"estimating":[68],"similarity.":[70],"However,":[71],"there":[72],"are":[73,182],"various":[74],"ways":[75],"how":[76,91],"information":[80],"combined":[83],"in":[84,123,157],"networks,":[86],"it":[88],"unclear":[90],"way":[93],"combining":[95],"affects":[96,155],"resulting":[98],"embeddings.In":[99],"this":[100],"paper,":[101],"we":[102,111,130,167],"run":[103],"experiment":[105],"on":[106,115,192],"movie":[107],"data,":[109],"where":[110],"analyze":[112],"effect":[114],"embedding":[116,128,147,164,175],"quality":[117],"caused":[118],"by":[119],"several":[120],"fusion":[121,153],"strategies":[122],"networks.":[125],"For":[126],"evaluating":[127],"quality,":[129],"propose":[131],"novel":[133],"measure,":[134],"Pair-Distance":[135],"Correlation,":[136],"which":[137],"quantifies":[138],"condition":[140],"that":[141,151,169,179],"similar":[142,146],"users":[143],"should":[144,196],"have":[145],"vectors.":[148],"We":[149],"find":[150,168],"strategy":[154],"results":[156],"terms":[158],"both":[160],"prediction":[161,170,194],"performance":[162,171],"quality.":[165,176],"Surprisingly,":[166],"not":[172],"necessarily":[173],"reflects":[174],"This":[177],"suggests":[178],"if":[180],"interest,":[184],"common":[186],"tendency":[187],"select":[189],"models":[190],"based":[191],"their":[193],"ability":[195],"reconsidered.":[198]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
