Training course determination method and device, processor and electronic equipment
The invention discloses a training course determination method and device, a processor and electronic equipment. The method comprises the steps of obtaining user feature data of a target user and course data of M candidate courses; for each candidate course, inputting the user feature data and the course data into a training rate prediction model, and processing to obtain a training rate of the target user for the candidate course; inputting the user feature data and the course data into a practice completion rate prediction model, and processing to obtain the practice completion rate of the target user for the candidate course; and fusing the practice starting rate and the practice finishing rate of each candidate course to obtain interest scores, sorting the M candidate courses according to a sequence of the interest scores of the candidate courses from high to low, determining a preset number of candidate courses in the front in a sorting result as target courses, and recommending the target courses to a target user. Through the method and the device, the problem of low accuracy of recommending training courses to users in related technologies is solved..
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Patent| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Europäisches Patentamt - (2023) vom: 29. Dez. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
WANG JUNBO [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Anmerkungen: |
Source: www.epo.org (no modifications made), First posted: 2023-12-29, Last update posted on www.tib.eu: 2024-03-13, Last updated: 2024-03-22 |
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| Patentnummer: |
CN117312629 |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
EPA00065681X |
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| 520 | |a The invention discloses a training course determination method and device, a processor and electronic equipment. The method comprises the steps of obtaining user feature data of a target user and course data of M candidate courses; for each candidate course, inputting the user feature data and the course data into a training rate prediction model, and processing to obtain a training rate of the target user for the candidate course; inputting the user feature data and the course data into a practice completion rate prediction model, and processing to obtain the practice completion rate of the target user for the candidate course; and fusing the practice starting rate and the practice finishing rate of each candidate course to obtain interest scores, sorting the M candidate courses according to a sequence of the interest scores of the candidate courses from high to low, determining a preset number of candidate courses in the front in a sorting result as target courses, and recommending the target courses to a target user. Through the method and the device, the problem of low accuracy of recommending training courses to users in related technologies is solved. | ||
| 650 | 4 | |a G06N: Computer systems based on specific computational models | |
| 650 | 4 | |a G16H: Healthcare informatics, i.e. information and communication technology [ict] specially adapted for the handling or processing of medical or healthcare data | |
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