{"id":"https://openalex.org/W4384625658","doi":"https://doi.org/10.1145/3539618.3592003","title":"Improving News Recommendation via Bottlenecked Multi-task Pre-training","display_name":"Improving News Recommendation via Bottlenecked Multi-task Pre-training","publication_year":2023,"publication_date":"2023-07-18","ids":{"openalex":"https://openalex.org/W4384625658","doi":"https://doi.org/10.1145/3539618.3592003"},"language":"en","primary_location":{"id":"doi:10.1145/3539618.3592003","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3592003","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","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/A5101771770","display_name":"X. H. Xiao","orcid":"https://orcid.org/0009-0004-3388-3935"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiongfeng Xiao","raw_affiliation_strings":["Aegis Information Technology Ltd. Co., Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-3388-3935","affiliations":[{"raw_affiliation_string":"Aegis Information Technology Ltd. Co., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100759941","display_name":"Qing Li","orcid":"https://orcid.org/0009-0008-1467-1573"},"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":"Qing Li","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-1467-1573","affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100690282","display_name":"Songlin Liu","orcid":"https://orcid.org/0009-0005-7791-0795"},"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":"Songlin Liu","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-7791-0795","affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5063459528","display_name":"Kun Zhou","orcid":"https://orcid.org/0000-0003-0650-9521"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Zhou","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0650-9521","affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3263,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64023465,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2082","last_page":"2086"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9977999925613403,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9884999990463257,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8402087688446045},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6266984343528748},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.613470196723938},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.5383473634719849},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4968469440937042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45569437742233276},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.44648268818855286},{"id":"https://openalex.org/keywords/news-aggregator","display_name":"News aggregator","score":0.41779518127441406},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.41164177656173706},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.35845261812210083},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.26980406045913696}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8402087688446045},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6266984343528748},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.613470196723938},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.5383473634719849},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4968469440937042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45569437742233276},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.44648268818855286},{"id":"https://openalex.org/C180505990","wikidata":"https://www.wikidata.org/wiki/Q498267","display_name":"News aggregator","level":2,"score":0.41779518127441406},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.41164177656173706},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35845261812210083},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.26980406045913696},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3539618.3592003","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3539618.3592003","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5299999713897705,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1602639018","https://openalex.org/W1967959079","https://openalex.org/W2109421086","https://openalex.org/W2153111836","https://openalex.org/W2742272831","https://openalex.org/W2963869731","https://openalex.org/W2970793364","https://openalex.org/W3034503922","https://openalex.org/W3117150731","https://openalex.org/W3152500092","https://openalex.org/W3155368131","https://openalex.org/W4240535087","https://openalex.org/W4285295485","https://openalex.org/W4404783772","https://openalex.org/W6714454088"],"related_works":["https://openalex.org/W3036238356","https://openalex.org/W2767445978","https://openalex.org/W2603387358","https://openalex.org/W2570892890","https://openalex.org/W3092831610","https://openalex.org/W2012785328","https://openalex.org/W2275988210","https://openalex.org/W2911655849","https://openalex.org/W4286432911","https://openalex.org/W3134737443"],"abstract_inverted_index":{"Recent":[0],"years":[1],"have":[2,28,66],"witnessed":[3],"the":[4,33,47,52,73,91,96,119,124,135,140,150,159],"boom":[5],"of":[6,20,143],"deep":[7],"neural":[8],"networks":[9],"in":[10],"online":[11],"news":[12,16,39,77,82,92,125,136,141,151],"recommendation":[13],"service.":[14],"As":[15],"articles":[17],"mainly":[18],"consist":[19],"textual":[21],"content,":[22],"pre-trained":[23,60],"language":[24],"models~(PLMs)":[25],"(e.g.":[26],"BERT)":[27],"been":[29,68],"widely":[30],"adopted":[31],"as":[32],"backbone":[34],"to":[35,45,89,117,133,138],"encode":[36],"them":[37],"into":[38,123],"embeddings,":[40],"which":[41,110],"would":[42],"be":[43,85],"utilized":[44],"generate":[46],"user":[48],"representations":[49],"or":[50,94],"perform":[51],"semantic":[53,121],"matching.":[54],"However,":[55],"existing":[56],"PLMs":[57],"are":[58],"mostly":[59],"on":[61,112,158],"large-scale":[62],"general":[63],"corpus,":[64],"and":[65,149,162],"not":[67,86],"specially":[69],"adapted":[70],"for":[71],"capturing":[72],"rich":[74],"information":[75,122],"within":[76],"articles.":[78],"Therefore,":[79],"their":[80],"produced":[81],"embeddings":[83],"may":[84],"informative":[87],"enough":[88],"represent":[90],"content":[93],"characterize":[95],"relations":[97],"among":[98],"news.":[99],"To":[100],"solve":[101],"it,":[102],"we":[103,128],"propose":[104],"a":[105],"bottlenecked":[106],"multi-task":[107],"pre-training":[108,131,170],"approach,":[109],"relies":[111],"an":[113],"information-bottleneck":[114],"encoder-decoder":[115],"architecture":[116],"compress":[118],"useful":[120],"embedding.":[126],"Concretely,":[127],"design":[129],"three":[130],"tasks,":[132],"enforce":[134],"embedding":[137],"recover":[139],"contents":[142],"itself,":[144],"its":[145],"frequently":[146],"oc-occurring":[147],"neighbours,":[148],"with":[152],"similar":[153],"topics.":[154],"We":[155],"conduct":[156],"experiments":[157],"MIND":[160],"dataset":[161],"show":[163],"that":[164],"our":[165],"approach":[166],"can":[167],"outperform":[168],"competitive":[169],"methods.":[171]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
