Technology for Recommending Optimum Learning Texts Based on Data Mining of Learning Historical Data
نویسندگان
چکیده
We are developing a bidirectional recommendation system that extracts the relationship among digital texts with historical logs, and recommends the optimum texts for learners using data mining methods, such as collaborative filtering. In this paper, we first discuss the bidirectional recommendation and then show results from an evaluation of actual use. Finally, we propose a method for a collaborative learning recommendation system that mines the data of similar users sharing non-favorite subjects using historical logs and user attribute data.
منابع مشابه
Prediction of Student Learning Styles using Data Mining Techniques
This paper focuses on the prediction of student learning styles using data mining techniques within their institutions. This prediction was aimed at finding out how different learning styles are achieved within learning environments which are specifically influenced by already existing factors. These learning styles, have been affected by different factors that are mainly engraved and found wit...
متن کاملLearning FCM by Data Mining in a Purchase System
Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrat...
متن کاملHigh-Dimensional Unsupervised Active Learning Method
In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...
متن کاملOptimizing Membership Functions using Learning Automata for Fuzzy Association Rule Mining
The Transactions in web data often consist of quantitative data, suggesting that fuzzy set theory can be used to represent such data. The time spent by users on each web page is one type of web data, was regarded as a trapezoidal membership function (TMF) and can be used to evaluate user browsing behavior. The quality of mining fuzzy association rules depends on membership functions and since t...
متن کاملSports Result Prediction Based on Machine Learning and Computational Intelligence Approaches: A Survey
In the current world, sports produce considerable statistical information about each player, team, games, and seasons. Traditional sports science believed science to be owned by experts, coaches, team managers, and analyzers. However, sports organizations have recently realized the abundant science available in their data and sought to take advantage of that science through the use of data mini...
متن کامل