Construction of New Intelligent Teaching Scene Based on Multimodal Wireless Sensing Technology

With the continuous development of computer technology and the gradual popularization of information technology application, the construction of intelligent teaching scene based on wireless sensing technology plays a more and more important role in modern information education. Taking a primary school as an example, this paper introduces multimodal wireless sensing technology into the construction of intelligent teaching system. The purpose of this paper is to explore the construction of a new teaching scene. Firstly, this paper deeply analyzes the sensing mechanism of wireless signal and optimizes the sensing mode, deployment structure, and signal processing in practical application, so that the system can run more effectively in the actual environment. Then, based on multimodal wireless sensing technology, this paper designs and optimizes the basic architecture and functions of intelligent teaching scene. The results show that combining the characteristic information of each mode to get the information conducive to identity confirmation, which can get better recognition performance and improve the accuracy. Combining the information of multiple modes can greatly improve the recognition performance. The user interest model combined with dynamic and static is used to optimize the system recommended resources, so that students can obtain high-quality and highly matched learning resources more quickly and accurately, so as to improve students’ learning efficiency in resource acquisition.

Improved Personalized Recommendation Algorithm Based on Context-Aware in Mobile Computing Environment

With the development and popularization of e-commerce and Internet, more and more attention has been paid to personalized recommendation for users. The traditional user interest model only considers the user’s behavior on the project, ignoring the user’s context at that time. Pointing to the shortage that context-related factors are not considered in previous works, combining the characteristics of a mobile computing environment, this paper studies the algorithm and model of mobile service recommendation. A recommendation algorithm based on specified context filtering in mobile computing environment is proposed. The context of the classification is aggregated, by grouping the scenarios of the same category together. Through experiments, we found that the improved personalized recommendation algorithms are superior to the common collaborative filtering algorithm.

A Personalized Matching System for Management Teaching Resources Based on Collaborative Filtering Algorithm

To realize education informatization, it is highly necessary to recommend teaching resources to students that can enhance their learning interest and improve teaching quality. This paper develops a personalized matching system for management teaching resources based on collaborative filtering (CF) algorithm. Firstly, the authors set up a user interest model, designed the flow and algorithm for personalized matching, and improved the similarity calculation method. Next, a personalized recommendation algorithm was developed based on the CF, and a personalized matching engine was constructed with the aid of Apache Mahout. The experimental results show that the proposed CF algorithm can effectively improve the recommendation quality, and push personalized teaching resources to each user; the learners are highly satisfied with the personalized matching system. The research results shed new light on personalized recommendation of teaching resources, opening up a new way to education informatization.

Search Unstructured Data Performance in P2P Networks Based on User Interest Model

Distributed (P2P) systems build up approximately coupled application-level overlays on high of the web to encourage affordable sharing of assets. They'll be generally delegated either organized or unstructured systems. while not requesting requirements over the topology, unstructured P2P systems is made appallingly speedily and ar so contemplated fitting to the web air. Be that as it may, the arbitrary pursuit strategies received by these systems now and then perform ineffectively with a larger than usual system Size. during this paper, we tend to search for to help the pursuit execution in unstructured P2P arranges through misusing clients' basic intrigue designs caught among a likelihood theoretic structure named the client intrigue model (UIM). an exploration convention and a directing table change convention ar increasingly arranged in order to speed up the hunt technique through self sorting out the P2P organize into somewhat world. Each hypothetical and test examination are Conducted and incontestable the viability and strength of our methodology.