{"id":"https://openalex.org/W3093242741","doi":"https://doi.org/10.1145/3394171.3413653","title":"What Aspect Do You Like","display_name":"What Aspect Do You Like","publication_year":2020,"publication_date":"2020-10-12","ids":{"openalex":"https://openalex.org/W3093242741","doi":"https://doi.org/10.1145/3394171.3413653","mag":"3093242741"},"language":"en","primary_location":{"id":"doi:10.1145/3394171.3413653","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394171.3413653","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","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/A5039502751","display_name":"Hao Jiang","orcid":"https://orcid.org/0000-0002-6902-9245"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Jiang","raw_affiliation_strings":["Shandong University, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong University, Qingdao, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100368524","display_name":"Wenjie Wang","orcid":"https://orcid.org/0000-0002-5199-1428"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Wenjie Wang","raw_affiliation_strings":["National University of Singapore, Singapore, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National University of Singapore, Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039731055","display_name":"Yinwei Wei","orcid":"https://orcid.org/0000-0003-1791-3159"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinwei Wei","raw_affiliation_strings":["Shandong University, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong University, Qingdao, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048947531","display_name":"Zan Gao","orcid":"https://orcid.org/0000-0003-2182-5741"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zan Gao","raw_affiliation_strings":["Shandong Artificial Intelligence Institute, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Artificial Intelligence Institute, Jinan, China","institution_ids":["https://openalex.org/I4210100255"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100703791","display_name":"Yinglong Wang","orcid":"https://orcid.org/0000-0002-8350-7186"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinglong Wang","raw_affiliation_strings":["Shandong Artificial Intelligence Institute, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Artificial Intelligence Institute, Jinan, China","institution_ids":["https://openalex.org/I4210100255"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038612499","display_name":"Liqiang Nie","orcid":"https://orcid.org/0000-0003-1476-0273"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liqiang Nie","raw_affiliation_strings":["Shandong University, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong University, Qingdao, China","institution_ids":["https://openalex.org/I154099455"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.9525,"has_fulltext":false,"cited_by_count":65,"citation_normalized_percentile":{"value":0.98211702,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3487","last_page":"3495"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9861000180244446,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9711999893188477,"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":0.8705193996429443},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5896215438842773},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5494843125343323},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4490443766117096},{"id":"https://openalex.org/keywords/recommender-system","display_name":"Recommender system","score":0.4185943305492401},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40237975120544434},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.389458566904068},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3752954304218292}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8705193996429443},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5896215438842773},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5494843125343323},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4490443766117096},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.4185943305492401},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40237975120544434},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.389458566904068},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3752954304218292},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3394171.3413653","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3394171.3413653","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 28th ACM International Conference on Multimedia","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":47,"referenced_works":["https://openalex.org/W178169250","https://openalex.org/W1982393615","https://openalex.org/W2013955429","https://openalex.org/W2020600820","https://openalex.org/W2101409192","https://openalex.org/W2135598826","https://openalex.org/W2140310134","https://openalex.org/W2433963325","https://openalex.org/W2512965516","https://openalex.org/W2512971201","https://openalex.org/W2605350416","https://openalex.org/W2723293840","https://openalex.org/W2741249238","https://openalex.org/W2765757915","https://openalex.org/W2766187676","https://openalex.org/W2766988981","https://openalex.org/W2770600170","https://openalex.org/W2798972759","https://openalex.org/W2896875776","https://openalex.org/W2897298014","https://openalex.org/W2897299615","https://openalex.org/W2897676901","https://openalex.org/W2913023375","https://openalex.org/W2936133855","https://openalex.org/W2949274928","https://openalex.org/W2949553967","https://openalex.org/W2953586472","https://openalex.org/W2955081857","https://openalex.org/W2955106599","https://openalex.org/W2956959730","https://openalex.org/W2964316331","https://openalex.org/W2965762068","https://openalex.org/W2965866655","https://openalex.org/W2966095354","https://openalex.org/W2974319653","https://openalex.org/W2981505961","https://openalex.org/W2981525000","https://openalex.org/W2981569692","https://openalex.org/W2981597278","https://openalex.org/W2982019227","https://openalex.org/W2982108874","https://openalex.org/W2982902390","https://openalex.org/W3097991661","https://openalex.org/W3111340645","https://openalex.org/W4231054948","https://openalex.org/W6610928041","https://openalex.org/W6712758098"],"related_works":["https://openalex.org/W4390273403","https://openalex.org/W4386781444","https://openalex.org/W3092950680","https://openalex.org/W4246980185","https://openalex.org/W2150182025","https://openalex.org/W4317039510","https://openalex.org/W3197542405","https://openalex.org/W2418190244","https://openalex.org/W4238861846","https://openalex.org/W3125580266"],"abstract_inverted_index":{"Online":[0],"micro-video":[1,141],"recommender":[2],"systems":[3],"aim":[4],"to":[5,75,87,119],"address":[6],"the":[7,14,20,33,126,134,137,155,169,181],"information":[8,105],"explosion":[9],"of":[10,52,157],"micro-videos":[11],"and":[12,41,101,124,140,168],"make":[13],"personalized":[15],"recommendation":[16],"for":[17,151],"users.":[18],"However,":[19],"existing":[21],"methods":[22],"still":[23],"have":[24],"some":[25],"limitations":[26],"in":[27,133],"learning":[28],"representative":[29],"user":[30,37,60,78,93,128,138],"interests,":[31,94],"since":[32],"multi-scale":[34,89],"time":[35,90],"effects,":[36],"interest":[38,71,79,114,122,129],"group":[39,72],"modeling,":[40],"false":[42],"positive":[43],"interactions":[44],"are":[45,143],"not":[46],"taken":[47],"into":[48,145],"consideration.":[49],"In":[50,65],"view":[51],"this,":[53],"we":[54,67,95,160],"propose":[55],"an":[56,70,113],"end-to-end":[57],"Multi-scale":[58],"Time-aware":[59],"Interest":[61],"modeling":[62],"Network":[63],"(MTIN).":[64],"particular,":[66],"first":[68],"present":[69],"routing":[73],"algorithm":[74],"generate":[76,125],"fine-grained":[77,121],"groups":[80,123],"based":[81],"on":[82,92,163],"user's":[83],"interaction":[84],"sequence.":[85],"Afterwards,":[86],"explore":[88],"effects":[91],"design":[96],"a":[97,146],"time-aware":[98],"mask":[99,115],"network":[100,116,149],"distill":[102],"multiple":[103],"temporal":[104,109],"by":[106],"several":[107],"parallel":[108],"masks.":[110],"And":[111],"then":[112],"is":[117],"introduced":[118],"aggregate":[120],"final":[127],"representation.":[130],"At":[131],"last,":[132],"prediction":[135],"unit,":[136],"representation":[139],"candidates":[142],"fed":[144],"deep":[147],"neural":[148],"(DNN)":[150],"predictions.":[152],"To":[153],"demonstrate":[154,172],"effectiveness":[156],"our":[158,174],"method,":[159],"conduct":[161],"experiments":[162],"two":[164],"publicly":[165],"available":[166],"datasets,":[167],"experimental":[170],"results":[171],"that":[173],"proposed":[175],"model":[176],"achieves":[177],"substantial":[178],"gains":[179],"over":[180],"state-of-the-art":[182],"methods.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":15},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}