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People rely increasingly on the internet to obtain news from a variety of sources due to the quick expansion of online information. However, this abundance leads to information overload, making it difficult for users to identify content that matches their interests. News Recommender Systems (NRS) aims to mitigate this issue by delivering personalized suggestions. An online data-mining, Deep Learning (DL)-based NRS is presented as a solution to the performance issues caused by ignoring user preferences in the recommendation process. This research proposes a hybrid architecture combining an Improved Collaborative Filtering Algorithm (ICFA) with a Hybrid Graph Neural Network (HGNN). The system module's core components are network function, database, user administration, and news recommendation. Experimental research was conducted using the News Click Behavior and Engagement Dataset from Kaggle, which contains user interaction logs including clicks, impressions, and engagement patterns across users and news articles. The data was preprocessed using normalization to scale features uniformly and enhance training stability. Additionally, Linear Discriminant Analysis (LDA) was employed for feature extraction to identify hidden topics within the news articles. The efficiency of the proposed model was evaluated based on ICFA-driven news recommendations tailored for both new and old users. The experimental findings show that the suggested method considerably enhances the metrics compared to the traditional methods on a practical dataset. The proposed model achieves 85.29% precision, 77.25% recall, and 81.87% F1 score, outperforming the robust baseline. These results confirm that the proposed HGNN-ICFA model delivers robust and personalized news recommendations across diverse user segments.

Under the implementation of new talent cultivation goals, the gaps in art design teaching are gradually revealed, and various teaching shortcomings have become stumbling blocks on the road of professional education development, which need to be improved in time. Combined with information technology, this paper proposes three curriculum integration strategies: enhancing audio-visual sensory effects, applying microteaching, and expanding the sharing channels of the art design resource library. A teaching resource application platform has been constructed for the art and design resource sharing strategy, which combines user-based and project-based collaborative filtering algorithms and is applied to the resource recommendation module. The performance of the system is evaluated through algorithmic comparison, and a satisfaction survey is conducted on the three dimensions of perceived usefulness, perceived ease of use, and intention to use for art and design students who use the system. The average satisfaction values in the three dimensions were 76.99%, 78.35%, and 79.5%, respectively, indicating that the art design students were more satisfied with the teaching resource application platform designed in this paper.

This paper proposes a personalized learning resource recommendation method based on dynamic collaborative filtering algorithm. Pearson correlation coefficient is used to calculate the data similarity between learning users or project resources in the network, and the unscored value is obtained. In order to solve the problems of sparse data and poor scalability in collaborative filtering algorithm, dynamic k-nearest-neighbor and Slope One algorithm are used to optimize it, and the sparsity of learning resource data in the network is analyzed according to the result of neighbor selection. The bidirectional self-equalization of stage evolution is used to improve the personalized recommendation of resource push, and the fuzzy adaptive binary particle swarm optimization algorithm based on the evolution state judgment is used to solve the problem of the optimal sequence recommendation, so as to realize the personalized learning resource recommendation. The experimental results show that the proposed method has higher matching degree and faster recommendation speed.

This paper proposes to introduce OBE theory in the curriculum map so as to build a curriculum master map based on the concept of engineering education accreditation-oriented and professional training objectives. In order to provide personalized services to students in the curriculum map, this paper applies the user-based collaborative filtering recommendation algorithm to the curriculum map and uses a similarity calculation based on Barclay’s coefficient improved similarity (BCCF) to finally implement the curriculum recommendation algorithm. The results show that the AUC of the recommendation algorithm constructed in this paper reaches 0.867. After the teaching reform, the mean values of students in the course reform class are 0.68, 0.61, and 1.31 higher than those in the non-course reform class at the level, of course, affect perception, teacher teaching, and student learning. Therefore, the OBE-based teaching method in this paper can develop personalized learning paths for students, which helps students learn better and plan the overall learning paths and make the most of educational resources.

Given the challenges of inter-domain information fusion and data sparsity in collaborative filtering algorithms, this paper proposes a cross-domain information fusion matrix decomposition algorithm to enhance the accuracy of personalized recommendations in artificial intelligence recommendation systems. The study begins by collecting Douban movie rating data and social network information. To ensure data integrity, Levenshtein distance detection is employed to remove duplicate scores, while natural language processing technology is utilized to extract keywords and topic information from social texts. Additionally, graph convolutional networks are utilized to convert user relationships into feature vectors, and a unique thermal coding method is used to convert discrete user and movie information into binary matrices. To prevent overfitting, the Ridge regularization method is introduced to gradually optimize potential feature vectors. Weighted average and feature connection techniques are then applied to integrate features from different fields. Moreover, the paper combines the item-based collaborative filtering algorithm with merged user characteristics to generate personalized recommendation lists.In the experimental stage, the paper conducts cross-domain information fusion optimization on four mainstream mathematical matrix decomposition algorithms: alternating least squares method, non-negative matrix decomposition, singular value decomposition, and latent factor model (LFM). It compares these algorithms with the non-fused approach. The results indicate a significant improvement in score accuracy, with mean absolute error and root mean squared error reduced by 12.8% and 13.2% respectively across the four algorithms. Additionally, when k = 10, the average F1 score reaches 0.97, and the ranking accuracy coverage of the LFM algorithm increases by 54.2%. Overall, the mathematical matrix decomposition algorithm combined with cross-domain information fusion demonstrates clear advantages in accuracy, prediction performance, recommendation diversity, and ranking quality, and improves the accuracy and diversity of the recommendation system. By effectively addressing collaborative filtering challenges through the integration of diverse techniques, it significantly surpasses traditional models in recommendation accuracy and variety.

With the deepening of cross-border e-commerce, the trend of buying and selling goods through the Internet is rising. It is necessary to establish a cross-border e-commerce platform that meets the above functions, and improve the ability to process big data in search. For example, the emergence of large amounts of data can not only help users make choices, but also increase the difficulty of users in choosing. At present, there are many problems in the big data search system in the market, such as inaccurate user personality analysis and low importance of product recommendation. E-commerce is developing rapidly in the new era, and new users are increasing every day. Many researchers invest in finding excellent cross-border e-commerce recommendation system as a business platform. The number of information in cross-border e-commerce shows a rapid growth pattern, and the rapid growth of data and information has seriously affected people's judgment. The big data search system based on collaborative filtering algorithm can meet the product recommendation system of cross-border e-commerce. The user matrix label is an attribute of construction. For the label quantification, the new user preference is the model of building the label, and the concept of weight is added to the label. The collaborative filtering algorithm works based on the created weight label.

With the development of information technology and the change of people’s education concept, personalized learning is getting more and more attention. The traditional classroom is difficult to realize the need of personalized English reading comprehension material recommendation, this paper designs a fusion English reading comprehension material recommendation system based on collaborative filtering algorithm and improved text similarity algorithm. Aiming at the problem that the traditional text similarity algorithm ignores the user’s differentiated attention to the information of English reading comprehension materials in text matching, the Inter-TF-IDF algorithm, which integrates the calculation of concentration and dispersion, is proposed. Combining this algorithm and the user-based collaborative filtering algorithm, the fusion recommendation system of this paper is proposed. The system utilizes the collaborative filtering algorithm to get the preliminary recommendation results of English reading comprehension materials, and then utilizes the improved Inter-TF-IDF algorithm to calculate the similarity between the text of the English reading comprehension materials and the text browsed by the user, and selects the English reading comprehension materials with a high degree of similarity as the final recommendation results. The overall recommendation accuracy of the system in this paper is maintained at a high level of 0.82-0.95. In the actual application effect, it significantly improves the English scores of students using the system, and obtains a high degree of satisfaction from the students. The recommendation system in this paper has a good promotion and application prospect in the field of English learning.

In this paper, with the help of big data analytics, students’ learning behavior patterns are deeply mined, so as to provide personalized learning support for students. The massive data generated by students in the learning process is first mined. Then the K-means algorithm is used to cluster the students’ behaviors. Finally, personalized push of learning resources for different types of learners based on collaborative filtering algorithm and customized learning path based on genetic algorithm. Research design teaching practice to verify the application effect of the method in this paper. Taking 150 students in a class of school A as an example, the collected behavioral data of 148 students are clustered and analyzed, which can be divided into 4 types of learners, and the method of this paper can recommend resources that meet the knowledge point needs and learning preferences of different groups of students, and recommend appropriate learning paths for 4 types of learners based on genetic algorithms. After practicing teaching, the average English score of the experimental class is 7.49 higher than that of the traditional teaching class (control class), and there is a significant difference (P=0.002). It shows that personalized teaching based on students’ learning behavior analysis can effectively improve the quality of English teaching.

This paper constructs a collection of effective teaching methods based on the improved K-Means clustering algorithm for clustering and dividing effective components of music teaching in colleges and universities. By analyzing the personalized content recommendation system, we can construct a recommendation system based on teaching content using information retrieval and filtering techniques. The collaborative filtering recommendation algorithm is used to ensure the accurate placement of teaching content. The Kruskal algorithm is used to find the minimum spanning tree of teaching effective components, and the K-means clustering principle is applied to the division of music teaching effective components, and the cluster of effective teaching components is divided by the clustering algorithm. According to the findings, mind-body integration and the teaching goal of valuing creativity were classified as effective teaching components in music. Personal aesthetics had a 0.6 influence on musical creativity, and a free environment had a 0.3 influence.

The characteristics of educational information, such as quantization, decentralization, redundancy and unstructuredness, bring complex and multi-level problems to the construction of high-quality educational resources in colleges and universities. The study constructs a model for the optimal allocation of educational resources in colleges and universities using a collaborative filtering algorithm. Taking big data as the background, the existing educational resources allocation ratio of colleges and universities is analyzed, and the current demand for resources in colleges and universities is predicted through the cosine similarity algorithm and the Pearson similarity algorithm, and the recommended resource items are derived and recommended. The study takes the university district of city Z as an example and uses the model in this paper to optimize the allocation of teaching and auxiliary room area, sports hall area, number of books, number of computers, and value of teaching instruments and equipment. After using the optimal allocation model to optimize the educational resources of colleges and universities, the pass rate of students’ exams increased to more than 70%, and the students were more satisfied with the subjective evaluation of the model. This paper’s model plays a significant role in the optimal allocation of educational resources, and colleges and universities can effectively utilize and reference it.