政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/150164

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/150164

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/150164

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150164

Title:	運用知識圖於公務人員的課程推薦 Course Recommendation for Civil Servant Based on Knowledge Graph
Authors:	謝政彥 Hsieh, Cheng-Yen
Contributors:	沈錳坤 Shan, Man-Kwan 謝政彥 Hsieh, Cheng-Yen
Keywords:	課程推薦知識圖公務人員 Course Recommendation Knowledge Graph Civil Servant
Date:	2024
Issue Date:	2024-03-01 13:40:16 (UTC+8)
Abstract:	公務人員為推動國家政策、提升國家競爭力，本職學能必須與時俱進。但公務人員每年在職研習的時間與次數有限，因此若能透過課程推薦技術，將可協助公務人員有效地選讀相關訓練課程。然而公務人員在數十年公務生涯中，會歷經不同服務機關、職等、職務等歷練，教育程度及年齡也會改變，因此課程推薦需考慮公務人員修課時背景，包括職務相關背景及影響喜好相關背景。而且一般成人學習的習慣是希望透過理解找到答案，因此課程推薦須具備可解釋性方能有效協助公務人員理解並進行選課決策。針對商品的推薦，現已有分別考慮情境背景或具備可解釋性的推薦技術。但較少有兩者兼具的研究。本研究提出一個運用知識圖的公務人員研習背景課程圖，並結合長短期記憶模型的推薦模型。此模型考量公務人員歷年的公務相關背景及個人人口統計背景。經實驗顯示本論文所提出的課程推薦模型，準確率極高且具有可解釋性。 In order to promote national policies and enhance national competitiveness, civil servants must keep pace with the times in their professional skills. However, the opportunities for job training in terms of time and frequency are limited for civil servants each year. Therefore, if course recommendation technology could be utilized, it could assist civil servants in effectively selecting relevant training courses. Over the decades-long career of a civil servant, they will experience different service institutions, ranks, and positions. Their education level and age will also change. Therefore, course recommendations need to take into account the background of civil servants, including job-related background and demographic background that affects preferences. Moreover, course recommendations must be explainable to effectively assist civil servants in understanding and making course selection decisions. Currently there are recommendation techniques that consider context or are explainable for product recommendation, but there is little research to have both. This thesis proposes a Civil-servant Profile Course Graph based on knowledge graph and integrated with the LSTM recommendation model. The proposed model makes recommendation by taking the job-related background and personal demographic background of civil servants into account. Experiments show that the proposed approach is highly accurate and explainable.
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Description:	碩士國立政治大學資訊科學系 109753207
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0109753207
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

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