We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
RESEARCH ON DATA MINING AND REINFORCEMENT LEARNING IN RECOMMENDATION SYSTEMS.
- Authors
YUERAN ZHAO; HUIYAN ZHAO
- Abstract
This paper aims to help students better grasp the required professional knowledge and core concepts. This paper presents a design method for a multi-layer knowledge base based on XML. According to learners' identity characteristics and learning behaviour, using the mathematical statistics method, the feature expression for the learning system is constructed. Multivariable linear regression theory establishes convergence constraints for accurate and deep mining. The average detection results of the collected samples are used for high-quality deep mining of user portraits in the learning system. This project intends to study the method of solving accurate confidence intervals for user portrait data in the education system. Excel and Access are used to complete the data collection of the teaching object and the construction of the database. A multi-mode interactive editing and processing method of user portrait information for education systems is studied in cloud computing. Finally, a learning system based on mathematical loading mode is proposed, and an object-oriented learning recommendation system is designed. The developed teaching software can enable students to get more teaching guidance when they acquire the required knowledge to improve students' learning effect effectively.
- Subjects
RECOMMENDER systems; INSTRUCTIONAL systems; DATA mining; DATABASES; REINFORCEMENT learning; MATHEMATICAL statistics
- Publication
Scalable Computing: Practice & Experience, 2024, Vol 25, Issue 3, p1914
- ISSN
1895-1767
- Publication type
Article
- DOI
10.12694/scpe.v25i3.2790