Personalized Education Resource Recommendation Method Based on Deep Learning in Intelligent Educational Robot Environments

Introduction

With the advancement of Internet technology, the development of a network education mode is gradually progressing in the direction of rich resources and diverse teaching methods; thus, the transformation of a higher education mode is receiving increasing attention (Coral & Bernuy, 2022). As more teachers and students embrace the online education cloud platform, classroom management is also exhibiting an information trend. Among these, the smart classroom is an important component of the current university information environment, and the platform also highlights the issue of information overload2 (Zhang, 2021). This paper aims to address this issue by employing a recommendation algorithm based on deep learning in the context of an intelligent educational robot. To address the issue of insufficient data extraction and low accuracy of the platform recommendation system, the XMAMBLSTM model is proposed based on an intelligent education robot environment. It consists of the pre-training layer XLNet for input information word vector, the Multi-Bi-LSTM layer for extracting context information, and the CRF layer for extracting entity information. The context information and entity relationship of the word vector are transferred to the next layer to achieve entity recognition and relationship extraction effectiveness.

There are two prerequisites for effective course information recommendation: information overload and the inability to precisely describe the demand with keywords. In the recommendation system, the collaborative filtering algorithm is one of the most important recommendation algorithms. The collaborative filtering algorithm is based on the idea that “people gather in groups, and things cluster together.” It requires no specialized knowledge and is simple to implement in engineering (Murad et al., 2020). Therefore, it has become the focus of many experts and scholars. However, the collaborative filtering-based recommendation algorithm faces the issues of a cold start and sparse data (Chae et al., 2020; Wei et al., 2020). It is unable to filter and recommend courses based on the actual circumstances of users, and its data scalability is low, so it cannot recommend effective courses to new users. The content recommendation algorithm can recommend courses that are highly relevant to the user’s interests. Azizi and Do (2018) proposed a recommendation algorithm based on content and collaborative filtering, which modeled users' interests and search targets. This method proposed a recommendation model based on an algorithm for content recommendation. However, this algorithm is incapable of dynamic adjustment, and there is also the issue that the recommended courses are overly professional; a hybrid algorithm was created by combining all types of recommendation algorithms. This method can partially compensate for the shortcomings of various recommendation algorithms. Mohammadpour et al. (2019) proposed a method for demand prediction based on a hybrid algorithm to obtain user contact and context information. However, this method has a number of drawbacks, including complicated calculations and low recommendation efficiency.

In recent years, deep learning has become the main tool for entity recognition, relationship extraction, and other tasks (Keser & Aghalarova, 2022; Shanshan et al., 2021; Tarus et al., 2018). As a result, incorporating deep learning into personalized recommendations can alleviate the issues caused by information overload, accurately extract the potential relationship between word vectors, and enhance the precision and diversity of recommendations.