{"id":"https://openalex.org/W4388777033","doi":"https://doi.org/10.1145/3625403.3625437","title":"Enhanced Semantic Matching with Topic-aware and Fine-grained User Modeling for News Recommendation","display_name":"Enhanced Semantic Matching with Topic-aware and Fine-grained User Modeling for News Recommendation","publication_year":2023,"publication_date":"2023-09-15","ids":{"openalex":"https://openalex.org/W4388777033","doi":"https://doi.org/10.1145/3625403.3625437"},"language":"en","primary_location":{"id":"doi:10.1145/3625403.3625437","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3625403.3625437","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology","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/A5102823715","display_name":"Zhengguang Wang","orcid":"https://orcid.org/0000-0003-1194-0922"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhengguang Wang","raw_affiliation_strings":["Shanghai LinkSure Network Technology Co., Ltd, Shanghai, China, China"],"raw_orcid":"https://orcid.org/0000-0003-1194-0922","affiliations":[{"raw_affiliation_string":"Shanghai LinkSure Network Technology Co., Ltd, Shanghai, China, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080516731","display_name":"Xia Fu","orcid":"https://orcid.org/0000-0001-7201-7694"},"institutions":[{"id":"https://openalex.org/I3133083760","display_name":"Sanda University","ror":"https://ror.org/00tp01q71","country_code":"CN","type":"education","lineage":["https://openalex.org/I3133083760"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xia Fu","raw_affiliation_strings":["School of Information Science and Technology, Sanda University, Shanghai, China, China"],"raw_orcid":"https://orcid.org/0000-0001-7201-7694","affiliations":[{"raw_affiliation_string":"School of Information Science and Technology, Sanda University, Shanghai, China, China","institution_ids":["https://openalex.org/I3133083760"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4314,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.70628678,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"189","last_page":"195"},"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/T10028","display_name":"Topic Modeling","score":0.9973000288009644,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.993399977684021,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8479011058807373},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.51167893409729},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4876796007156372},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.3675750494003296}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8479011058807373},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.51167893409729},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4876796007156372},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.3675750494003296},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3625403.3625437","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3625403.3625437","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 2nd International Conference on Algorithms, Data Mining, and Information Technology","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":21,"referenced_works":["https://openalex.org/W2094286023","https://openalex.org/W2903803738","https://openalex.org/W2950416834","https://openalex.org/W2963869731","https://openalex.org/W2964536660","https://openalex.org/W2970793364","https://openalex.org/W3014828506","https://openalex.org/W3034236656","https://openalex.org/W3034503922","https://openalex.org/W3103448498","https://openalex.org/W3154275079","https://openalex.org/W3168247839","https://openalex.org/W3186240982","https://openalex.org/W3189967866","https://openalex.org/W3190660689","https://openalex.org/W3199369553","https://openalex.org/W3215799567","https://openalex.org/W4224903831","https://openalex.org/W4240935049","https://openalex.org/W4290945693","https://openalex.org/W4295884249"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"Online":[0],"news":[1,16,34,45,81,84,101,148],"articles":[2,17],"contain":[3],"representative":[4],"textual":[5,50],"content":[6],"including":[7],"title,":[8],"abstract,":[9],"category":[10],"and":[11,13,35,68,82,86,106],"entities,":[12],"the":[14,53,100,104,107,111,123,127,135,145],"clicked":[15],"by":[18,71],"users":[19],"indicate":[20],"their":[21,87],"interests.":[22,37],"Fully":[23],"exploiting":[24],"these":[25],"features":[26],"is":[27],"critical":[28],"for":[29,126],"accurate":[30],"matching":[31,61,70,125],"between":[32,78],"candidate":[33,80],"user":[36,66,128],"In":[38],"this":[39],"paper,":[40],"we":[41,56,115],"propose":[42,57],"a":[43,58,79,94,117],"topic-aware":[44],"encoder":[46,97],"to":[47,63],"enhance":[48],"rich":[49],"representations":[51],"of":[52,147],"news.":[54],"Moreover,":[55],"unified":[59,95],"semantic":[60,102],"framework":[62],"model":[64],"fine-grained":[65],"interests":[67],"user-news":[69],"explicitly":[72],"modeling":[73],"both":[74],"high-order":[75],"feature":[76],"interactions":[77],"user\u2019s":[83],"history,":[85],"temporal":[88],"dependency,":[89],"which":[90],"are":[91],"integrated":[92],"into":[93],"transformer":[96],"with":[98],"considering":[99],"representation,":[103],"type":[105],"position":[108],"embeddings":[109],"as":[110],"input":[112],"embeddings.":[113],"Then":[114],"introduce":[116],"single-layer":[118],"neural":[119],"network":[120],"based":[121],"on":[122,134],"interest":[124],"click-through":[129],"rate":[130],"prediction.":[131],"Extensive":[132],"experiments":[133],"benchmark":[136],"dataset":[137],"MIND":[138],"show":[139],"that":[140],"it":[141],"can":[142],"effectively":[143],"improve":[144],"performance":[146],"recommendation.":[149]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}