Applying Adaptive Hybrid Recommendation Technology on Search System of English Learning Articles

• Main Authors: Kuo, Feng-jung, 郭豐榮
• Other Authors: Cheng, Shu-chen
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/54106792353966005863

碩士 === 南台科技大學 === 資訊工程系 === 101 === Along with advanced network technology, if you want to search for information, the first thought must be through the network. Use the search engine, you can find a lot of information, but it caused “information overload”. Therefore, in the study system created "essence of the article" block, automatically removes a lot of advertising and online bookstore information, leaving only about teaching the article. A hybrid recommended method to give learners adaptive recommendation is proposed in this study; hybrid recommended is to use ‘Content-based recommendation’ and ‘Collaborative filtering’ these two methods to recommend. The Content-based in this study, Term Frequency - Inverse Document Frequency (TF-IDF) method is used to calculate the feature of articles. And then, use the Neural Text Categorizer (NTC) to training keyword bank. In the training process, this study proposes adjustment method to extend and revise the weights in keyword bank. After training is completed, use this keyword bank weights as recommended score. The Collaborative filtering in this study, use User-based to do calculations user preferences with high similarity users, and then combine ‘Group recommendation’ to analysis group features and adjustment user’s recommend items with user’s preferences. This research used hybrid recommendation to provide learners adaptive recommendation, according to each learner has different abilities and interests to give the recommended articles, so that each learner can read article meet their needs. The experimental results show a high accuracy in the recommendations articles.