一個以本體論為基礎的自學影片推薦機制

Abstract

知識經濟的時代要如何創造有價值的知識，藉由知識分享與管理進而提供有效的學習，已成為一個重要的議題。影片具有比文字更容易使人理解的特性，因此愈來愈多人採用影片做為自學的媒介。然而，目前大部分的自學影片是預先製作的，故內容無法包含自學者本身的使用經驗。隨著網際網路普及化，自學者利用網路搜尋學習資源的需求將會持續增長。然而，目前的推薦系統缺乏一個可以提供推薦自學影片的推薦機制。故本研究提出一個以本體論為基礎的自學影片推薦機制，此機制是結合以項目為基礎的過濾、內容式過濾及協同過濾的混合式方法，透過本體論分析使用者正在瀏覽的自學影片，並結合使用者過去對於自學影片的相關回饋，將其他關聯使用者興趣的自學影片推薦給使用者。本研究針對提出的混合式推薦機制實作一自學影片推薦網站，並與其他相關推薦機制進行準確度的比較。實驗結果證明本研究提出的機制較其他推薦機制能將準確度提升57%，能更準確的推薦適合的自學影片給使用者。

How to create valuable knowledge and provide users for self-study through knowledge sharing and knowledge management in the knowledge-driven economy times has become an important issue. As the video is easier to understand for people than the text, more and more people use video as their self-study medium. But the present self-study videos are mainly pre-production, they can’t consist of learners’ self-study experience. With Internet getting more popular, the need to obtain information through search engine when learning something will increase than before. However, there are only a few video recommendation mechanism adapted to the self-study environment. In this paper, we propose an ontology-based self-study video recommendation mechanism (OSVRM) which integrates adopted ontology item-based filtering, content-based filtering, and collaborative filtering. According to the ontology of the current self-study video and the user feedback of the past self-study videos, OSVRM takes charge of analyzing and recommending self-study videos to users that correspond with their interests. We also implement a web-based system to accomplish the OSVRM and compare to other recommendation mechanism. Our experiment results show that the OSVRM can enhance 57% accuracy. Therefore, the OSVRM manifests its own better recommendation than conventional recommendation mechanism.