News recommendation method based on comparative learning
The invention discloses a news recommendation method based on comparative learning. The method comprises a user interest extraction step based on comparative learning; the user interest extraction step comprises the following steps: providing a user interest encoder, wherein the user interest encode...
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| creator | ZHENG HAITAO XIA SHUTAO XIAO XI LI MINGCHAO LIU HAOZHUANG JIANG YONG |
| description | The invention discloses a news recommendation method based on comparative learning. The method comprises a user interest extraction step based on comparative learning; the user interest extraction step comprises the following steps: providing a user interest encoder, wherein the user interest encoder is configured to encode a news sequence browsed by a user to obtain an interest vector; encoding the news sequence browsed by the user to obtain a first interest vector; performing data enhancement on the news sequence browsed by the user, and encoding the news sequence after data enhancement to obtain a second interest vector; training the user interest encoder, and in the training process, introducing interest comparison learning loss which enables the first interest vector to be close to the second interest vector and enables the first interest vector to be far away from interest vectors of other users.
本发明公开了基于对比学习的新闻推荐方法，包括基于对比学习的用户兴趣抽取步骤；所述用户兴趣抽取步骤包括：提供一用户兴趣编码器，该用户兴趣编码器被配置为对用户浏览的新闻序列进行编码得到兴趣向量；对所述用户浏览的新闻序列进 |
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本发明公开了基于对比学习的新闻推荐方法，包括基于对比学习的用户兴趣抽取步骤；所述用户兴趣抽取步骤包括：提供一用户兴趣编码器，该用户兴趣编码器被配置为对用户浏览的新闻序列进行编码得到兴趣向量；对所述用户浏览的新闻序列进</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220802&DB=EPODOC&CC=CN&NR=114840747A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76419</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220802&DB=EPODOC&CC=CN&NR=114840747A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHENG HAITAO</creatorcontrib><creatorcontrib>XIA SHUTAO</creatorcontrib><creatorcontrib>XIAO XI</creatorcontrib><creatorcontrib>LI MINGCHAO</creatorcontrib><creatorcontrib>LIU HAOZHUANG</creatorcontrib><creatorcontrib>JIANG YONG</creatorcontrib><title>News recommendation method based on comparative learning</title><description>The invention discloses a news recommendation method based on comparative learning. The method comprises a user interest extraction step based on comparative learning; the user interest extraction step comprises the following steps: providing a user interest encoder, wherein the user interest encoder is configured to encode a news sequence browsed by a user to obtain an interest vector; encoding the news sequence browsed by the user to obtain a first interest vector; performing data enhancement on the news sequence browsed by the user, and encoding the news sequence after data enhancement to obtain a second interest vector; training the user interest encoder, and in the training process, introducing interest comparison learning loss which enables the first interest vector to be close to the second interest vector and enables the first interest vector to be far away from interest vectors of other users.
本发明公开了基于对比学习的新闻推荐方法，包括基于对比学习的用户兴趣抽取步骤；所述用户兴趣抽取步骤包括：提供一用户兴趣编码器，该用户兴趣编码器被配置为对用户浏览的新闻序列进行编码得到兴趣向量；对所述用户浏览的新闻序列进</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>HANDLING RECORD CARRIERS</subject><subject>PHYSICS</subject><subject>PRESENTATION OF DATA</subject><subject>RECOGNITION OF DATA</subject><subject>RECORD CARRIERS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLDwSy0vVihKTc7PzU3NS0ksyczPU8hNLcnIT1FISixOTVEA8oGSBYlFQLmyVIWc1MSivMy8dB4G1rTEnOJUXijNzaDo5hri7KGbWpAfn1pckJicmpdaEu_sZ2hoYmFiYG5i7mhMjBoALW0uoQ</recordid><startdate>20220802</startdate><enddate>20220802</enddate><creator>ZHENG HAITAO</creator><creator>XIA SHUTAO</creator><creator>XIAO XI</creator><creator>LI MINGCHAO</creator><creator>LIU HAOZHUANG</creator><creator>JIANG YONG</creator><scope>EVB</scope></search><sort><creationdate>20220802</creationdate><title>News recommendation method based on comparative learning</title><author>ZHENG HAITAO ; XIA SHUTAO ; XIAO XI ; LI MINGCHAO ; LIU HAOZHUANG ; JIANG YONG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN114840747A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>HANDLING RECORD CARRIERS</topic><topic>PHYSICS</topic><topic>PRESENTATION OF DATA</topic><topic>RECOGNITION OF DATA</topic><topic>RECORD CARRIERS</topic><toplevel>online_resources</toplevel><creatorcontrib>ZHENG HAITAO</creatorcontrib><creatorcontrib>XIA SHUTAO</creatorcontrib><creatorcontrib>XIAO XI</creatorcontrib><creatorcontrib>LI MINGCHAO</creatorcontrib><creatorcontrib>LIU HAOZHUANG</creatorcontrib><creatorcontrib>JIANG YONG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHENG HAITAO</au><au>XIA SHUTAO</au><au>XIAO XI</au><au>LI MINGCHAO</au><au>LIU HAOZHUANG</au><au>JIANG YONG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>News recommendation method based on comparative learning</title><date>2022-08-02</date><risdate>2022</risdate><abstract>The invention discloses a news recommendation method based on comparative learning. The method comprises a user interest extraction step based on comparative learning; the user interest extraction step comprises the following steps: providing a user interest encoder, wherein the user interest encoder is configured to encode a news sequence browsed by a user to obtain an interest vector; encoding the news sequence browsed by the user to obtain a first interest vector; performing data enhancement on the news sequence browsed by the user, and encoding the news sequence after data enhancement to obtain a second interest vector; training the user interest encoder, and in the training process, introducing interest comparison learning loss which enables the first interest vector to be close to the second interest vector and enables the first interest vector to be far away from interest vectors of other users.
本发明公开了基于对比学习的新闻推荐方法，包括基于对比学习的用户兴趣抽取步骤；所述用户兴趣抽取步骤包括：提供一用户兴趣编码器，该用户兴趣编码器被配置为对用户浏览的新闻序列进行编码得到兴趣向量；对所述用户浏览的新闻序列进</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
| title | News recommendation method based on comparative learning |
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