User recommendation model training method and device, computer equipment and storage medium
The invention relates to a user recommendation model training method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a plurality of user pairsfrom a database as training samples; Determining a sample target value of each training sample and a...
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creator | CAI CHENGJIA |
description | The invention relates to a user recommendation model training method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a plurality of user pairsfrom a database as training samples; Determining a sample target value of each training sample and a target level of the sample target value according to the interaction type of the user pair; Extracting sample characteristics of the training samples to obtain corresponding sample characteristics; Inputting the sample characteristics into a recommendation model, wherein the recommendation model determines the sequence of each training sample through the sample characteristics; When the matching rate of the sequence of each training sample and the target level reaches the preset accuracy, the trained recommendation model is obtained, and through the recommendation model trained in this way, the passive party with the high matching degree can serve as the recommendation user of the active party, so that the recommen |
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The method comprises the following steps: acquiring a plurality of user pairsfrom a database as training samples; Determining a sample target value of each training sample and a target level of the sample target value according to the interaction type of the user pair; Extracting sample characteristics of the training samples to obtain corresponding sample characteristics; Inputting the sample characteristics into a recommendation model, wherein the recommendation model determines the sequence of each training sample through the sample characteristics; When the matching rate of the sequence of each training sample and the target level reaches the preset accuracy, the trained recommendation model is obtained, and through the recommendation model trained in this way, the passive party with the high matching degree can serve as the recommendation user of the active party, so that the recommen</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; HANDLING RECORD CARRIERS ; PHYSICS ; PRESENTATION OF DATA ; RECOGNITION OF DATA ; RECORD CARRIERS</subject><creationdate>2019</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=20190618&DB=EPODOC&CC=CN&NR=109902753A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20190618&DB=EPODOC&CC=CN&NR=109902753A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>CAI CHENGJIA</creatorcontrib><title>User recommendation model training method and device, computer equipment and storage medium</title><description>The invention relates to a user recommendation model training method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a plurality of user pairsfrom a database as training samples; Determining a sample target value of each training sample and a target level of the sample target value according to the interaction type of the user pair; Extracting sample characteristics of the training samples to obtain corresponding sample characteristics; Inputting the sample characteristics into a recommendation model, wherein the recommendation model determines the sequence of each training sample through the sample characteristics; When the matching rate of the sequence of each training sample and the target level reaches the preset accuracy, the trained recommendation model is obtained, and through the recommendation model trained in this way, the passive party with the high matching degree can serve as the recommendation user of the active party, so that the recommen</description><subject>CALCULATING</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>2019</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNy70KwjAUxfEuDqK-w3VXqBaRjlIUJyedHMolOdZA82Fy4_MbxAdwOsP5_6bV_ZYQKUJ5a-E0i_GOrNcYSSIbZ9xAFvL0mthp0ngbhRWVPGQpEq9sQpHyvZP4yAOK0CbbeTV58Jiw-O2sWp6O1-68RvA9UmAFB-m7y6Zu23q73zWH5p_mA53FO8s</recordid><startdate>20190618</startdate><enddate>20190618</enddate><creator>CAI CHENGJIA</creator><scope>EVB</scope></search><sort><creationdate>20190618</creationdate><title>User recommendation model training method and device, computer equipment and storage medium</title><author>CAI CHENGJIA</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN109902753A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2019</creationdate><topic>CALCULATING</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>CAI CHENGJIA</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>CAI CHENGJIA</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>User recommendation model training method and device, computer equipment and storage medium</title><date>2019-06-18</date><risdate>2019</risdate><abstract>The invention relates to a user recommendation model training method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a plurality of user pairsfrom a database as training samples; Determining a sample target value of each training sample and a target level of the sample target value according to the interaction type of the user pair; Extracting sample characteristics of the training samples to obtain corresponding sample characteristics; Inputting the sample characteristics into a recommendation model, wherein the recommendation model determines the sequence of each training sample through the sample characteristics; When the matching rate of the sequence of each training sample and the target level reaches the preset accuracy, the trained recommendation model is obtained, and through the recommendation model trained in this way, the passive party with the high matching degree can serve as the recommendation user of the active party, so that the recommen</abstract><oa>free_for_read</oa></addata></record> |
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language | chi ; eng |
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subjects | CALCULATING COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
title | User recommendation model training method and device, computer equipment and storage medium |
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