Collaborative graph convolution and learning style-based course recommendation method
The invention discloses a course recommendation method based on a graph convolutional network and a learning style, and the method comprises the following steps: 1, predicting a score, coding course embedded information connected with a learner into first-order embedded information of the learner, a...
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| creator | ZHANG GUODAO GE YISU WANG CHAOCHAO LU YANJIE ZHANG HAQI GAO XIAOYUN YE HAIYANG |
| description | The invention discloses a course recommendation method based on a graph convolutional network and a learning style, and the method comprises the following steps: 1, predicting a score, coding course embedded information connected with a learner into first-order embedded information of the learner, and obtaining high-order embedded information through the first-order embedded information; aggregating each order of embedded information in the high-order embedded information into a single vector through an aggregation function to obtain an aggregation embedded representation; performing inner product operation on the aggregation embedded representation of the learner and the course to obtain a predicted score of the learner on the course; 2, respectively defining a learner summary and a course summary, calculating a learning style vector, and obtaining a course learning style similarity score according to the learner learning style vector and the course learning style vector; and step 3, optimizing the predictio |
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aggregating each order of embedded information in the high-order embedded information into a single vector through an aggregation function to obtain an aggregation embedded representation; performing inner product operation on the aggregation embedded representation of the learner and the course to obtain a predicted score of the learner on the course; 2, respectively defining a learner summary and a course summary, calculating a learning style vector, and obtaining a course learning style similarity score according to the learner learning style vector and the course learning style vector; and step 3, optimizing the predictio</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS</subject><creationdate>2024</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=20240827&DB=EPODOC&CC=CN&NR=118551115A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25543,76294</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240827&DB=EPODOC&CC=CN&NR=118551115A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHANG GUODAO</creatorcontrib><creatorcontrib>GE YISU</creatorcontrib><creatorcontrib>WANG CHAOCHAO</creatorcontrib><creatorcontrib>LU YANJIE</creatorcontrib><creatorcontrib>ZHANG HAQI</creatorcontrib><creatorcontrib>GAO XIAOYUN</creatorcontrib><creatorcontrib>YE HAIYANG</creatorcontrib><title>Collaborative graph convolution and learning style-based course recommendation method</title><description>The invention discloses a course recommendation method based on a graph convolutional network and a learning style, and the method comprises the following steps: 1, predicting a score, coding course embedded information connected with a learner into first-order embedded information of the learner, and obtaining high-order embedded information through the first-order embedded information; aggregating each order of embedded information in the high-order embedded information into a single vector through an aggregation function to obtain an aggregation embedded representation; performing inner product operation on the aggregation embedded representation of the learner and the course to obtain a predicted score of the learner on the course; 2, respectively defining a learner summary and a course summary, calculating a learning style vector, and obtaining a course learning style similarity score according to the learner learning style vector and the course learning style vector; and step 3, optimizing the predictio</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>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyjEKwjAUBuAuDqLeIR6gQ5CCqwTFyUnn8pr8toHkvZCkBW-viAdw-pZv3TyMhECDZKp-gRozpUlZ4UXCXL2wInYqgDJ7HlWpr4B2oAL3SXMuUBlWYgQ7-vaIOonbNqsnhYLdz02zv5zv5toiSY-SyIJRe3PT-th1WuvudPjnvAFUUzoU</recordid><startdate>20240827</startdate><enddate>20240827</enddate><creator>ZHANG GUODAO</creator><creator>GE YISU</creator><creator>WANG CHAOCHAO</creator><creator>LU YANJIE</creator><creator>ZHANG HAQI</creator><creator>GAO XIAOYUN</creator><creator>YE HAIYANG</creator><scope>EVB</scope></search><sort><creationdate>20240827</creationdate><title>Collaborative graph convolution and learning style-based course recommendation method</title><author>ZHANG GUODAO ; 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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | Collaborative graph convolution and learning style-based course recommendation method |
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