METHOD AND SYSTEM OF REAL-TIME GRAPH-BASED EMBEDDING FOR PERSONALIZED CONTENT RECOMMENDATION
Disclosed are a real-time graph-based embedding construction method for personalized content recommendation and a system thereof. The embedding method for personalized recommendation comprises the steps of: generating a graph representing a relationship between content to be recommended; learning em...
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creator | SUNG NAKO KIM MINKYU SIM SANG KWON KIM KYUNG MIN |
description | Disclosed are a real-time graph-based embedding construction method for personalized content recommendation and a system thereof. The embedding method for personalized recommendation comprises the steps of: generating a graph representing a relationship between content to be recommended; learning embedding of the content by using the graph; and performing embedding of a user by reflecting the activities of a user related to the content in the graph on which the learning is completed.
개인화 컨텐츠 추천을 위한 실시간 그래프기반 임베딩 구축 방법 및 시스템이 개시된다. 개인화 추천을 위한 임베딩 방법은, 추천 대상이 되는 컨텐츠 간의 관계를 나타내는 그래프를 생성하는 단계; 상기 그래프를 이용하여 상기 컨텐츠의 임베딩을 학습하는 단계; 및 상기 학습이 완료된 그래프에 상기 컨텐츠와 관련된 사용자의 활동을 반영하여 상기 사용자의 임베딩을 수행하는 단계를 포함한다. |
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개인화 컨텐츠 추천을 위한 실시간 그래프기반 임베딩 구축 방법 및 시스템이 개시된다. 개인화 추천을 위한 임베딩 방법은, 추천 대상이 되는 컨텐츠 간의 관계를 나타내는 그래프를 생성하는 단계; 상기 그래프를 이용하여 상기 컨텐츠의 임베딩을 학습하는 단계; 및 상기 학습이 완료된 그래프에 상기 컨텐츠와 관련된 사용자의 활동을 반영하여 상기 사용자의 임베딩을 수행하는 단계를 포함한다.</description><language>eng ; kor</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2021</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=20210209&DB=EPODOC&CC=KR&NR=102214422B1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20210209&DB=EPODOC&CC=KR&NR=102214422B1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SUNG NAKO</creatorcontrib><creatorcontrib>KIM MINKYU</creatorcontrib><creatorcontrib>SIM SANG KWON</creatorcontrib><creatorcontrib>KIM KYUNG MIN</creatorcontrib><title>METHOD AND SYSTEM OF REAL-TIME GRAPH-BASED EMBEDDING FOR PERSONALIZED CONTENT RECOMMENDATION</title><description>Disclosed are a real-time graph-based embedding construction method for personalized content recommendation and a system thereof. The embedding method for personalized recommendation comprises the steps of: generating a graph representing a relationship between content to be recommended; learning embedding of the content by using the graph; and performing embedding of a user by reflecting the activities of a user related to the content in the graph on which the learning is completed.
개인화 컨텐츠 추천을 위한 실시간 그래프기반 임베딩 구축 방법 및 시스템이 개시된다. 개인화 추천을 위한 임베딩 방법은, 추천 대상이 되는 컨텐츠 간의 관계를 나타내는 그래프를 생성하는 단계; 상기 그래프를 이용하여 상기 컨텐츠의 임베딩을 학습하는 단계; 및 상기 학습이 완료된 그래프에 상기 컨텐츠와 관련된 사용자의 활동을 반영하여 상기 사용자의 임베딩을 수행하는 단계를 포함한다.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>PHYSICS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2021</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNi0EKwjAQAHvxIOofFjwXbOwHkmbbBpvdkuylIpQi8SRaqP_HHnyApznMzDa7eZSWLWiyEIco6IFrCKi7XJxHaILu29zoiBbQG7TWUQM1B-gxRCbdueuqKiZBknWs2Hskq8Ux7bPNY3ou6fDjLjvWKFWbp_k9pmWe7umVPuMlFCelirJUypji_F_1BV4zMx0</recordid><startdate>20210209</startdate><enddate>20210209</enddate><creator>SUNG NAKO</creator><creator>KIM MINKYU</creator><creator>SIM SANG KWON</creator><creator>KIM KYUNG MIN</creator><scope>EVB</scope></search><sort><creationdate>20210209</creationdate><title>METHOD AND SYSTEM OF REAL-TIME GRAPH-BASED EMBEDDING FOR PERSONALIZED CONTENT RECOMMENDATION</title><author>SUNG NAKO ; KIM MINKYU ; SIM SANG KWON ; KIM KYUNG MIN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_KR102214422BB13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; kor</language><creationdate>2021</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>PHYSICS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>SUNG NAKO</creatorcontrib><creatorcontrib>KIM MINKYU</creatorcontrib><creatorcontrib>SIM SANG KWON</creatorcontrib><creatorcontrib>KIM KYUNG MIN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>SUNG NAKO</au><au>KIM MINKYU</au><au>SIM SANG KWON</au><au>KIM KYUNG MIN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>METHOD AND SYSTEM OF REAL-TIME GRAPH-BASED EMBEDDING FOR PERSONALIZED CONTENT RECOMMENDATION</title><date>2021-02-09</date><risdate>2021</risdate><abstract>Disclosed are a real-time graph-based embedding construction method for personalized content recommendation and a system thereof. The embedding method for personalized recommendation comprises the steps of: generating a graph representing a relationship between content to be recommended; learning embedding of the content by using the graph; and performing embedding of a user by reflecting the activities of a user related to the content in the graph on which the learning is completed.
개인화 컨텐츠 추천을 위한 실시간 그래프기반 임베딩 구축 방법 및 시스템이 개시된다. 개인화 추천을 위한 임베딩 방법은, 추천 대상이 되는 컨텐츠 간의 관계를 나타내는 그래프를 생성하는 단계; 상기 그래프를 이용하여 상기 컨텐츠의 임베딩을 학습하는 단계; 및 상기 학습이 완료된 그래프에 상기 컨텐츠와 관련된 사용자의 활동을 반영하여 상기 사용자의 임베딩을 수행하는 단계를 포함한다.</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
title | METHOD AND SYSTEM OF REAL-TIME GRAPH-BASED EMBEDDING FOR PERSONALIZED CONTENT RECOMMENDATION |
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