Abstract
This study provides an in-depth study and analysis of English course recommendation techniques through a combination of bee colony algorithm and neural network algorithm. In this study, the acquired text is trained with a document vector by a deep learning model and combined with a collaborative filtering method to recommend suitable courses for users. Based on the analysis of the current research status and development of the technology related to course resource recommendation, the deep learning technology is applied to the course resource recommendation based on the current problems of sparse data and low accuracy of the course recommendation. For the problem that the importance of learning resources to users changes with time, this study proposes to fuse the time information into the neural collaborative filtering algorithm through the clustering classification algorithm and proposes a deep learning-based course resource recommendation algorithm to better recommend the course that users want to learn at this stage promptly. Secondly, the course cosine similarity calculation model is improved for the course recommendation algorithm. Considering the impact of the number of times users rate courses and the time interval between users rating different courses on the course similarity calculation, the contribution of active users to the cosine similarity is reduced and a time decay penalty is given to users rating courses at different periods. By improving the hybrid recommendation algorithm and similarity calculation model, the error value, recall, and accuracy of course recommendation results outperform other algorithmic models. The requirements analysis identifies the personalized online teaching system with rural primary and secondary school students as the main service target and then designs the overall architecture and functional modules of the recommendation system and the database table structure to implement the user registration, login, and personal center functional modules, course publishing, popular recommendation, personalized recommendation, Q&A, and rating functional modules.
Highlights
With the progress of science and technology, people have developed a variety of population intelligence optimization algorithms, such as artificial bee colony algorithm, pigeon colony algorithm, and cat colony algorithm, by studying the social behaviour of bees, birds, ants, and other organisms in nature, and have used them to solve many complex optimization problems [1]. ese algorithms search through the cooperation among individuals of the population with high flexibility and robustness and are widely used in many fields such as automated storage systems, pattern recognition, and controller tuning
In the field of machine learning, an integrated learning classification method based on the artificial bee colony algorithm for tuning parameters is constructed to make the classifier model have higher
Computational Intelligence and Neuroscience classification accuracy; in the field of the air logistics industry, the multi-objective artificial bee colony algorithm is used to optimize the cargo space allocation problem of the automated storage system in the actual transportation process, and for the problems of locally optimal solutions and low convergence accuracy of the algorithm itself, an improved artificial bee colony algorithm based on the group collaboration model is proposed and applied to improve the operational efficiency of logistics allocation in airport cargo terminals [3]. erefore, the research on this topic is of great importance for the wider application of intelligent algorithms
Summary
With the progress of science and technology, people have developed a variety of population intelligence optimization algorithms, such as artificial bee colony algorithm, pigeon colony algorithm, and cat colony algorithm, by studying the social behaviour of bees, birds, ants, and other organisms in nature, and have used them to solve many complex optimization problems [1]. ese algorithms search through the cooperation among individuals of the population with high flexibility and robustness and are widely used in many fields such as automated storage systems, pattern recognition, and controller tuning. As the research progresses, the shortcomings of the related algorithms are slowly highlighted, such as the sensitivity of the algorithm itself to the initial population, convergence accuracy, and ease to fall into local and other shortcomings How to optimize these drawbacks in combination with practical problems has become a research hotspot [2]. Traditional collaborative filtering algorithms are combined with deep learning to ensure better recommendation resources, since courses and people’s learning priorities change over time, to ensure that the recommendations are “up-to-date” and personalized for the users. We use clustering and statistics to process time-assisted information and fuse neural collaborative filtering algorithms to reduce the impact of “old courses” on users’ current course recommendations, increasing the accuracy of the overall recommendations
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