| Summary: | 碩士 === 國立屏東科技大學 === 資訊管理系所 === 106 === With advanced of Information Technology, everyone can upload and retrieve data on the Internet. There is too much data on the internet that make user is hard to find the necessary data on the internet. This issue is called Information Overloading. Through the recommender system recommend items for users can make users spending less time to find the necessary information on the internet. The recommender system collected the user’s behaviors, and by the user’s behaviors to recommend the items to the users they are interested in. In this research, we collected the brainwave signal by EEG and the comments from the videos, and then develop a recommender system based on the comment and EEG signals. Through this recommender system, the user can get the information more correspondingly. We used Logistic regression to construct the EEG-Preference model, and then combining with the analysis results that the comment’s sentiment by the Natural Language Processing. This recommender system will according to the user’s EEG-Preference model and the sentiment of the video’s comments to recommend the items to users. The experimental results show that the recommender system combined with brain wave signal and sentiment analysis of the videos’ comments can get good recommendation results.
|
|---|
