近年來，推薦系統(Recommendation system)相關議題吸引了許多專家學者的目光，最主要是因網路的蓬勃發展，造成了傳統消費行為的改變，其中協同過濾是推薦系統中採用最廣泛的推薦技術，藉由其他和你相似的使用者的偏好，去預測你的個人偏好，進而達到個人化的推薦效果。但傳統的協同過濾是將不同用戶的興趣，同等考慮，因為現實生活中用戶的偏好是會經常改變的，這就使得在某一段時間的偏好改變對於整個項目中，會顯得並不突出，因此本研究提出一個基於考慮順序效應之協同過濾推薦方法，所以在考量用戶間相似度的時候，同時考慮偏好的順序性。研究結果顯示，考慮偏好順序性的協同過濾推薦方法，可以提高推薦系統預測的正確性。 In recent years, the recommendation system has attracted the attention of many experts and scholars, mainly because of the vigorous development of the Internet, which has caused the change of traditional consumption behavior. The collaborative filtering is the most widely used recommendation technology in the recommendation system , By other similar users and your preferences, to predict your personal preferences, and then achieve the personalized recommendation effect. But the traditional collaborative filtering is the interest of different users, the same considerations, because the real life of the user''s preferences will often change, which makes a certain period of time to change the preferences for the entire project, will appear not prominent, so In this study, we propose a collaborative filtering recommendation method based on the sequential effect. Therefore, considering the similarity between users, we consider the order of preference. The results show that the recommended method of cooperative filtering is considered to improve the correctness of the proposed system.
