{"id":"https://openalex.org/W4403420846","doi":"https://doi.org/10.1109/icws62655.2024.00100","title":"LLM-MHR: A LLM-Augmented Multimodal Hashtag Recommendation Algorithm","display_name":"LLM-MHR: A LLM-Augmented Multimodal Hashtag Recommendation Algorithm","publication_year":2024,"publication_date":"2024-07-07","ids":{"openalex":"https://openalex.org/W4403420846","doi":"https://doi.org/10.1109/icws62655.2024.00100"},"language":"en","primary_location":{"id":"doi:10.1109/icws62655.2024.00100","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icws62655.2024.00100","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Web Services (ICWS)","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/A5042812101","display_name":"Zhijie Tan","orcid":"https://orcid.org/0000-0003-4934-1001"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhijie Tan","raw_affiliation_strings":["Peking University,School of Software and Microelectronics,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Peking University,School of Software and Microelectronics,Beijing,China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113396923","display_name":"Yuzhi Li","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuzhi Li","raw_affiliation_strings":["Peking University,School of Software and Microelectronics,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Peking University,School of Software and Microelectronics,Beijing,China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044895531","display_name":"Xiang Yuan","orcid":"https://orcid.org/0009-0004-2687-8943"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Yuan","raw_affiliation_strings":["Peking University,School of Software and Microelectronics,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Peking University,School of Software and Microelectronics,Beijing,China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064163477","display_name":"Shengwei Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengwei Meng","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,School of Computer Science,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,School of Computer Science,Beijing,China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100415560","display_name":"Weiping Li","orcid":"https://orcid.org/0000-0002-5882-8527"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiping Li","raw_affiliation_strings":["Peking University,School of Software and Microelectronics,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Peking University,School of Software and Microelectronics,Beijing,China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059356240","display_name":"Tong Mo","orcid":"https://orcid.org/0000-0002-3564-4610"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tong Mo","raw_affiliation_strings":["Peking University,School of Software and Microelectronics,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Peking University,School of Software and Microelectronics,Beijing,China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5042812101"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":2.3469,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.91179131,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"810","last_page":"821"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12016","display_name":"Web Data Mining and Analysis","score":0.9933000206947327,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9933000206947327,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9688000082969666,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9621999859809875,"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":0.7611837387084961},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3615663945674896}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7611837387084961},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3615663945674896}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icws62655.2024.00100","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icws62655.2024.00100","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Web Services (ICWS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":70,"referenced_works":["https://openalex.org/W1647217513","https://openalex.org/W2027323723","https://openalex.org/W2739671343","https://openalex.org/W2783366437","https://openalex.org/W2787126526","https://openalex.org/W2801586761","https://openalex.org/W2905317234","https://openalex.org/W2911319979","https://openalex.org/W2921907837","https://openalex.org/W3000507134","https://openalex.org/W3027879771","https://openalex.org/W3044438666","https://openalex.org/W3094502228","https://openalex.org/W3153451655","https://openalex.org/W3154527660","https://openalex.org/W3156470785","https://openalex.org/W3175603587","https://openalex.org/W3185341429","https://openalex.org/W3198377975","https://openalex.org/W4254415482","https://openalex.org/W4296591867","https://openalex.org/W4307079201","https://openalex.org/W4312310776","https://openalex.org/W4312651322","https://openalex.org/W4323556869","https://openalex.org/W4360890239","https://openalex.org/W4366850747","https://openalex.org/W4372272503","https://openalex.org/W4376311940","https://openalex.org/W4376632920","https://openalex.org/W4385245566","https://openalex.org/W4385718034","https://openalex.org/W4386071547","https://openalex.org/W4386075647","https://openalex.org/W4386187806","https://openalex.org/W4386392742","https://openalex.org/W4386561866","https://openalex.org/W4386858468","https://openalex.org/W4389519118","https://openalex.org/W4390962670","https://openalex.org/W4391128584","https://openalex.org/W4391136507","https://openalex.org/W4391766565","https://openalex.org/W4393147129","https://openalex.org/W4393147304","https://openalex.org/W4393160124","https://openalex.org/W4393161084","https://openalex.org/W4393406988","https://openalex.org/W4396736086","https://openalex.org/W4396827020","https://openalex.org/W4396913059","https://openalex.org/W4400606525","https://openalex.org/W4402670080","https://openalex.org/W4403220611","https://openalex.org/W6665213384","https://openalex.org/W6777615688","https://openalex.org/W6784333009","https://openalex.org/W6838849837","https://openalex.org/W6846966473","https://openalex.org/W6847076894","https://openalex.org/W6850593144","https://openalex.org/W6851398480","https://openalex.org/W6851950068","https://openalex.org/W6852635484","https://openalex.org/W6852764809","https://openalex.org/W6853009676","https://openalex.org/W6856893839","https://openalex.org/W6860743466","https://openalex.org/W6861581687","https://openalex.org/W6866641287"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890"],"abstract_inverted_index":{"The":[0,27],"recommendation":[1,32,121],"of":[2,29,39,75,160,171,184,203,212,240],"suitable":[3,109],"hashtags":[4,110],"for":[5,15,118],"mi-croposts":[6],"encompassing":[7],"multimodal":[8,30,40,119,137],"content":[9,106],"stands":[10],"as":[11,23,85],"a":[12,72,136,161,223,243],"pivotal":[13],"challenge":[14],"numerous":[16],"Social":[17],"Networking":[18],"Service":[19],"(SNS)":[20],"applications":[21],"such":[22,51],"Instagram,":[24],"Weibo,":[25],"etc.":[26],"accuracy":[28],"hashtag":[31,120],"algorithms":[33],"relies":[34],"heavily":[35],"on":[36,50,254],"the":[37,46,79,104,158,169,182,190,210,220,255],"comprehension":[38],"information,":[41,44],"user":[42,149],"historical":[43,62,150,155,163,206],"and":[45,63,107,231],"reasoning":[47,93,113],"ability":[48],"based":[49],"information.":[52],"However,":[53,115],"most":[54],"previous":[55],"works":[56],"have":[57],"not":[58],"effectively":[59],"utilized":[60],"both":[61,229],"additional":[64,99,185],"information":[65,100,151,164],"simultaneously.":[66],"Large":[67],"Language":[68],"Models":[69],"(LLMs)":[70],"learn":[71],"vast":[73],"amount":[74],"implicit":[76],"knowledge":[77,87],"during":[78],"pre-training":[80],"stage,":[81],"which":[82],"can":[83,97],"serve":[84],"potential":[86],"bases":[88],"while":[89,147],"also":[90],"possessing":[91],"strong":[92,112],"abilities.":[94],"Therefore,":[95],"LLMs":[96,117,127,140,176,213],"provide":[98],"to":[101,134,144,167,214,236],"help":[102],"understand":[103],"micropost":[105],"infer":[108],"with":[111,266],"ability.":[114],"introducing":[116],"faces":[122],"three":[123],"main":[124],"challenges.":[125],"Firstly,":[126],"require":[128],"an":[129,197],"efficient":[130,198],"modality":[131,199],"alignment":[132,200],"module":[133,166,201],"accept":[135],"input.":[138],"Secondly,":[139],"are":[141],"highly":[142],"sensitive":[143],"input":[145,172,215],"order,":[146],"utilizing":[148,228],"requires":[152],"accepting":[153],"multiple":[154,205],"samples,":[156,207],"necessitating":[157,181],"design":[159],"robust":[162],"processing":[165,204],"eliminate":[168],"influence":[170],"order.":[173],"Thirdly,":[174],"fine-tuning":[175,239],"entails":[177],"substantial":[178],"computational":[179],"overheads,":[180],"reduction":[183],"trainable":[186],"parameters.":[187],"To":[188,218],"address":[189],"first":[191],"two":[192],"challenges,":[193],"this":[194],"paper":[195],"designs":[196],"capable":[202],"simultaneously":[208],"addressing":[209],"sensitivity":[211],"order":[216],"changes.":[217],"tackle":[219],"third":[221],"challenge,":[222],"hybrid":[224],"prompt":[225],"learning":[226],"approach":[227],"soft":[230],"hard":[232],"prompts":[233],"is":[234,250],"proposed":[235],"achieve":[237],"parameter-efficient":[238],"LLMs.":[241],"Finally,":[242],"LLM-augmented":[244],"Multimodal":[245],"Hashtag":[246],"Recommendation":[247],"algorithm":[248],"(LLM-MHR)":[249],"implemented.":[251],"Comprehensive":[252],"experiments":[253],"representative":[256],"dataset":[257],"MACON":[258],"demonstrate":[259],"that":[260],"LLM-MHR":[261],"has":[262],"achieved":[263],"SOTA":[264],"performances":[265],"significant":[267],"improvements.":[268]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}