{"id":"https://openalex.org/W2735241201","doi":"https://doi.org/10.1109/ijcnn.2017.7965998","title":"Incorporating message embedding into co-factor matrix factorization for retweeting prediction","display_name":"Incorporating message embedding into co-factor matrix factorization for retweeting prediction","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2735241201","doi":"https://doi.org/10.1109/ijcnn.2017.7965998","mag":"2735241201"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7965998","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7965998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100428572","display_name":"Can Wang","orcid":"https://orcid.org/0000-0002-2890-0057"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Can Wang","raw_affiliation_strings":["Chinese Academy of Sciences, Institute of Automation, Beijing, China","University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024206808","display_name":"Qiudan Li","orcid":"https://orcid.org/0000-0002-8714-4562"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiudan Li","raw_affiliation_strings":["Chinese Academy of Sciences, Institute of Automation, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100436099","display_name":"Lei Wang","orcid":"https://orcid.org/0000-0003-1810-3019"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Wang","raw_affiliation_strings":["Chinese Academy of Sciences, Institute of Automation, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038521974","display_name":"Daniel Zeng","orcid":"https://orcid.org/0000-0002-9046-222X"},"institutions":[{"id":"https://openalex.org/I138006243","display_name":"University of Arizona","ror":"https://ror.org/03m2x1q45","country_code":"US","type":"education","lineage":["https://openalex.org/I138006243"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Daniel Dajun Zeng","raw_affiliation_strings":["Chinese Academy of Sciences, Institute of Automation, Beijing, China","Department of Management Information Systems, University of Arizona, Tucson, Arizona, USA","University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Institute of Automation, Beijing, China","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]},{"raw_affiliation_string":"Department of Management Information Systems, University of Arizona, Tucson, Arizona, USA","institution_ids":["https://openalex.org/I138006243"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100428572"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":5.0507,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.95756758,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1265","last_page":"1272"},"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.998199999332428,"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/T11478","display_name":"Caching and Content Delivery","score":0.9972000122070312,"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":0.8357070684432983},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.8268382549285889},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.7429873943328857},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.6547589302062988},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6116850972175598},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.508427083492279},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.48137953877449036},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.43350082635879517},{"id":"https://openalex.org/keywords/factor","display_name":"Factor (programming language)","score":0.4258456230163574},{"id":"https://openalex.org/keywords/matrix-representation","display_name":"Matrix representation","score":0.42330318689346313},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4070553183555603},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37533456087112427},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3098853528499603}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8357070684432983},{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.8268382549285889},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.7429873943328857},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.6547589302062988},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6116850972175598},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.508427083492279},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.48137953877449036},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.43350082635879517},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.4258456230163574},{"id":"https://openalex.org/C103275481","wikidata":"https://www.wikidata.org/wiki/Q6787889","display_name":"Matrix representation","level":3,"score":0.42330318689346313},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4070553183555603},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37533456087112427},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3098853528499603},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7965998","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7965998","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W170896097","https://openalex.org/W1025179915","https://openalex.org/W1614298861","https://openalex.org/W1969245231","https://openalex.org/W2016420487","https://openalex.org/W2080817720","https://openalex.org/W2094948131","https://openalex.org/W2098655743","https://openalex.org/W2117420919","https://openalex.org/W2135598826","https://openalex.org/W2139750075","https://openalex.org/W2153579005","https://openalex.org/W2157881433","https://openalex.org/W2468369286","https://openalex.org/W2471920251","https://openalex.org/W2472158773","https://openalex.org/W2474765392","https://openalex.org/W2475334473","https://openalex.org/W2487744644","https://openalex.org/W2508504774","https://openalex.org/W2510317721","https://openalex.org/W2512931297","https://openalex.org/W2515144511","https://openalex.org/W2532065816","https://openalex.org/W2919115771","https://openalex.org/W3099466270","https://openalex.org/W3099732023","https://openalex.org/W3102701984","https://openalex.org/W3102895136","https://openalex.org/W4294170691","https://openalex.org/W4300528642","https://openalex.org/W6606940212","https://openalex.org/W6626540732","https://openalex.org/W6682691769"],"related_works":["https://openalex.org/W2127243424","https://openalex.org/W2037504162","https://openalex.org/W4390394189","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2539013788","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2972997031","https://openalex.org/W2075222291"],"abstract_inverted_index":{"With":[0],"the":[1,27,38,87,123,129,132,145,160,163,176],"rapid":[2],"growth":[3],"of":[4,71,82,140,162,179,193],"Web":[5],"2.0,":[6],"social":[7,65],"media":[8],"has":[9],"become":[10],"a":[11,93,110,154,203],"prevalent":[12],"information":[13,31,63,84,134,183],"sharing":[14],"and":[15,42,64,90,102,135,148,181,209],"spreading":[16],"platform,":[17],"where":[18],"users":[19],"can":[20],"retweet":[21],"interesting":[22],"messages.":[23],"To":[24],"better":[25],"understand":[26],"propagation":[28],"mechanism":[29],"for":[30,115,206],"diffusion,":[32],"it":[33,120],"is":[34,75],"necessary":[35],"to":[36,77,85],"model":[37,165],"user":[39,62],"retweeting":[40,50,96,118,195],"behavior":[41,97],"predict":[43],"future":[44],"retweets.":[45],"Some":[46],"existing":[47],"work":[48],"in":[49],"prediction":[51,73],"based":[52,137,152],"on":[53,57,153,166],"matrix":[54,104,147,151],"factorization":[55,105],"focuses":[56],"using":[58],"user-message":[59,146],"interaction":[60],"information,":[61,67],"influence":[66],"etc.":[68],"The":[69,197],"challenge":[70],"improving":[72],"performance":[74,161],"how":[76],"jointly":[78,143],"perform":[79],"deep":[80],"representation":[81,139,178],"these":[83],"solve":[86],"sparsity":[88],"problem":[89],"then":[91],"learn":[92],"more":[94,190],"comprehensive":[95],"model.":[98,156],"Inspired":[99],"by":[100,127],"word2vec":[101,136],"co-factor":[103],"model,":[106,112],"this":[107],"paper":[108],"proposes":[109],"hybrid":[111],"called":[113],"HCFMF,":[114],"learning":[116],"users'":[117,194],"behavior,":[119],"first":[121],"computes":[122],"message":[124,130],"content":[125,182],"similarity":[126,150],"considering":[128],"co-occurrence,":[131],"author":[133,180],"low-dimensional":[138],"content,":[141],"then,":[142],"decomposes":[144],"message-message":[149],"co-factorization":[155],"We":[157],"empirically":[158],"evaluate":[159],"proposed":[164],"real":[167],"world":[168],"weibo":[169],"datasets.":[170],"Experimental":[171],"results":[172],"show":[173],"that":[174],"taking":[175],"dense":[177],"into":[184],"consideration":[185],"could":[186,200],"allow":[187],"us":[188],"make":[189],"accurate":[191],"analysis":[192],"patterns.":[196],"mined":[198],"patterns":[199],"serve":[201],"as":[202],"feedback":[204],"channel":[205],"both":[207],"consumers":[208],"management":[210],"departments.":[211]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":4}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}