Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

Recommender system for ubiquitous learning based on decision tree

Abstract: In recent years, the fast development of mobile, wireless communication and sensor technologies has provided new possibilities for supporting learning activities. Ubiquitous learning, which is learning that can take place anywhere and anytime, is the best example. In order to provide learners with adequate learning experience, factors such learner's characteristics and context should be considered. Managing the learner context can help delivering the best resource adaptation services. Learning object proposed to the learner is obtained from contexual informations using the decision tree model. On the present paper, a recommender system for ubiquitous learning using context information of the learner and a decision tree model is presented, and k-fold cross validation is used in the experiment for estimating and validating the performance of our recommender system for ubiquitous learning.

Alternatives to the Tree Model for Extensive Games

Abstract: The interaction between the moves of the players, the information available for each move and the outcome of chance moves, is described for finite games by the well known tree model due to Kuhn [2]. Among the reasons for considering alternative models of extensive games are (i) A desire to weaken the finiteness requirement and the need to handle the measurability problems which then arise. (ii) The natural way in which extensive games often present themselves is an input-output description. Even in the finite case, to derive a tree model from such a description requires the general solution of the closed loop relations, which can be demanding. (iii) For several simple but important conclusions concerning the effect of information changes in games, the tree model is already more detailed than necessary and may even hide the simplicity of the situation. On the other hand, the matrix form of the game does not contain enough detail to permit formulation of even such a concept as “open-loop strategy”.

Tree-Based Real-Time Advertisement Recommendation System in Online Broadcasting

Abstract: The viewing time of media content per week through TV is still dominant. Users are exposed to numerous advertisements, such as commercials, electronic home shopping, product placement (PPL), and T-Commerce while watching TV programs. Most of the advertisement systems provide a good overview of products. However, traditional advertising services do not consider user preferences, meaning it is difficult to expect anything more than mere exposure to them. We can adopt a recommendation system to predict the preference. However, existing recommendation systems find it difficult to satisfy the real-time requirements of online broadcasting because of the large overhead incurred in preference prediction processes. In this paper, we propose a real-time recommendation system to provide personalized advertisements. The proposed system generates tree models based on user historical data. To reduce the overhead of preference prediction, we introduce a sorted HashMap that enables fast tree searches. For sophisticated preference prediction, the proposed system normalizes the users’ preferences by considering the characteristics of their tree model. Finally, we conduct experiments to evaluate the performance of the proposed tree-based recommendation system.