Rating Matrix Research Articles

Enhanced Collaborative Filtering: Combining Autoencoder and Opposite User Inference to Solve Sparsity and Gray Sheep Issues

In recent years, the study of recommendation systems has become crucial, capturing the interest of scientists and academics worldwide. Music, books, movies, news, conferences, courses, and learning materials are some examples of using the recommender system. Among the various strategies employed, collaborative filtering stands out as one of the most common and effective approaches. This method identifies similar active users to make item recommendations. However, collaborative filtering has two major challenges: sparsity and gray sheep. Inspired by the remarkable success of deep learning across a multitude of application areas, we have integrated deep learning techniques into our proposed method to effectively address the aforementioned challenges. In this paper, we present a new method called Enriched_AE, focused on autoencoder, a well-regarded unsupervised deep learning technique renowned for its superior ability in data dimensionality reduction, feature extraction, and data reconstruction, with an augmented rating matrix. This matrix not only includes real users but also incorporates virtual users inferred from opposing ratings given by real users. By doing so, we aim to enhance the accuracy of predictions, thus enabling more effective recommendation generation. Through experimental analysis of the MovieLens 100K dataset, we observe that our method achieves notable reductions in both RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error), underscoring its superiority over the state-of-the-art collaborative filtering models.

A service-recommendation method for the Internet of Things leveraging implicit social relationships

Integrating social-relationship information is widely considered an effective means to addressing the sparsity issue in Internet-of-Things (IoT) service recommendations. However, owing to platform variations and privacy concerns, acquiring explicit social-relationship information has become challenging. Therefore, researchers are gradually focusing on leveraging implicit social-relationship information to enhance recommendation effectiveness. Nevertheless, when using implicit social relationships, both user/service-implicit social-relationship and user-service interaction information rely on the same rating matrix, leading to nonindependence and coupling, which influence the recommendation model. To address this challenge, the present paper introduces a unique approach for IoT service recommendations, leveraging implicit social-relationship information (short for ISoc-IoTRec). First, we construct a user-service interaction graph, user-implicit social-relationship graph, and service-implicit social-relationship graph, learning their node embeddings through graph neural networks (GNNs). Subsequently, we introduce a cross information control module to achieve feature separation, ensuring that the user and service embeddings learned from different graphs remain independent in representation, thereby alleviating the nonindependence and coupling issues arising from the same data source. Following feature separation, the user and service embeddings are aggregated separately. Through an attention mechanism module, the model can selectively emphasize or attenuate the impact of each feature while considering the overall information, further addressing nonindependence and coupling issues. Extensive experiments conducted on three real-world datasets underscore the remarkable performance of ISoc-IoTRec, significantly outperforming existing recommendation algorithms.

A study on hybrid recommend system combined sentiment analysis with matrix factorization

Contemporary research endeavors have evinced a substantial interest in integrating heterogeneous data sources within unified recommendation system frameworks. Concomitantly, the conventional two-dimensional product-user rating matrix ubiquitous in matrix factorization problems is being augmented by incorporating ancillary dimensions such as sentiment, temporality, and spatial characteristics. Concurrently, the challenge of surmounting limitations in capturing Vietnamese sentiment characteristics for data enrichment has garnered scholarly attention. Stemming from these two salient issues, the authors propound a hybrid model that amalgamates the factor matrix principle from collaborative filtering methodologies with sentiment analysis for prognosticating user rating propensities. Through empirical evaluation on a corpus of mobile application reviews, the proposed model has demonstrated its suitability for research purposes and exhibited superior predictive accuracy compared to simpler paradigms.

A tensor recommendation method based on HMM network and meta-path

The main approach to capturing user latent preferences in recommendation systems (RS) is through high-order tensor decomposition and the deep-walk method. Several key issues, if solved, could improve the performance of RS. These include enforcing the interpretation of RS in the context of sparse data completion, cold start, and interpretability, mining user latent preferences with a tensor constructed from a user-item rating matrix (RM) and a preference match mechanism based on K-nearest neighbor (KNN) similar users. In this paper, a method that integrates a hidden Markov model, meta-path, and third-order tensor (HMM-MP-TOT) is proposed. An HMM, based on the user-item RM and latent preferences from KNN users is constructed. Subsequently, the Viterbi and deep-walk methods are used to obtain a series of user-item two-dimensional MPs. Then, truncated − singular value decomposition (t-SVD) is applied to a user-item-KNN third-order tensor to obtain a better recommendation result. On average, HMM-MP-TOT obtains 94.7% precision, 80.2% recall, and 96.4% diversity.

Preferences for Text Messaging Supports During Youth Transition to Adult Mental Health Services: Theory-Informed Modified e-Delphi Study.

For many young people, the transition from child to adult mental health services is a vulnerable time associated with treatment disengagement and illness progression. Providing service information and options to youth, appealing to them, and tailoring to their needs during this period could help overcome systematic barriers to a successful transition. We know little about how SMS text message-based interventions might be leveraged to support the motivational, informational, and behavioral needs of youth during this time. Ascertaining youth preferences for the content and functionality of an SMS text message service could inform prototype development. This study investigated consensus preferences among youth on important content, technology features, and engagement supports to inform a transition-focused SMS text message service. A modified e-Delphi survey design was used to collect demographics, current levels of technology use, importance ratings on message content, preferred technical features, and barriers and enablers to engagement for youth in Canada aged 16-26 years who have accessed mental health services within the past 5 years. Survey items on content were categorized according to the information-motivation-behavioral skills (IMB) model. Survey items on technical features were categorized according to the persuasive system design (PSD) model. A predefined consensus rating matrix and descriptive statistics were used to characterize the sample. The high consensus threshold was 70%. A total of 100 participants, predominantly non-White (n=47, 47%), aged 20-26 years (n=59, 59%), and who had first accessed mental health services between the ages of 13 and 19 years (n=60, 60%), were selected. The majority (n=90, 90%) identified as daily SMS text message users. A high level of consensus on importance ratings was reported in 45% (9/20) of content items based on the IMB model. There were higher levels of consensus on importance ratings related to behavior domain items (3/3, 100%) than information domain items (4/9, 44%) or motivation domain items (2/8, 25%). A high level of consensus on importance ratings was reported in only 19% (4/21) of feature and functionality items based on the PSD model. Among PSD model categories, there was a high level of consensus on importance ratings in 8% (1/12) of the primary task support domain items and 100% (3/3) of the system credibility support domain items. None of the dialogue-support and social-support domain items met the high level of consensus thresholds. In total, 27% (27/100) of youth indicated that the most significant enabler for engaging with a transition-focused SMS text message intervention was the personalization of text messages. Scientists developing next-generation SMS text messaging interventions for this population need to consider how levels of consensus on different features may impact feasibility and personalization efforts. Youth can (and should) play an integral role in the development of these interventions.

Efficient and adaptive secure cross-domain recommendations

In recommendation systems, cross-domain recommendation (CDR) has emerged as an effective method for solving the problem of data sparsity and cold start. However, challenges such as data heterogeneity, data security, and recommendation accuracy are faced by existing CDR methods. An efficient and adaptive secure cross-domain recommendation (EAAS-CDR) model is proposed. First, the Automatic Encoder–Decoder (AutoED) model is proposed to address variations in user–item interaction quantity and item characteristics across various domains. The AutoED model enables adaptive and secure cross-domain recommendations by modeling user preferences across various domains, unifying vector dimensions, and avoiding the release of the rating matrix. An efficient cross-domain privacy protection method is proposed to resist data leakage. To ensure feature invariance for knowledge transfer, the inherent low perturbation characteristics of the Johnson–Lindenstrauss Transform mechanism are leveraged and combined with the output of our encoder. Finally, domain relevance measurement metrics and a domain fusion algorithm are proposed to use the source domain knowledge in the target domain and improve the recommendation accuracy of the model. Experimental results demonstrate that our approach is not only more efficient but also more secure than existing CDR approaches.

A novel fuzzy neural collaborative filtering for recommender systems

In collaborative filtering recommendations, part of studies mainly focus on quantitatively learning user preferences based on explicit rating information. However, due to the uncertainty and complexity of decision-making scenarios, user preferences usually express ambiguity and diversity. Therefore, to fully capture user preferences from both qualitative and quantitative perspectives and improve the accuracy and reliability of recommendations, we propose a novel fuzzy neural collaborative filtering for recommender systems. Firstly, we qualitatively divide user preferences into fine-grained categories based on picture fuzzy set and transform the rating matrix into four preference degree matrices. Then, we employ Bernoulli matrix factorization to independently learn preference features of each submatrix to obtain picture fuzzy numbers. To enhance the adaptability of picture fuzzy set in recommender systems, we design an improved scoring function based on picture fuzzy number to effectively distinguish differences in user preference levels. In addition, we use a simple deep neural framework to quantitatively extract global preferences of users and fuse it with fine-grained preferences of users to thoroughly predict user preferences. Experiments on four benchmark datasets show that the proposed model outperforms other comparison methods in terms of recommendation accuracy. Compared with the closest competitor method, our model has the highest improvements of 5.08% and 4.04% in the metrics HR@10 and NDCG@10, respectively.

MOOCs video recommendation using low-rank and sparse matrix factorization with inter-entity relations and intra-entity affinity information

PurposeThe serious information overload problem of MOOCs videos decreases the learning efficiency of the students and the utilization rate of the videos. There are two problems worthy of attention for the matrix factorization (MF)-based video learning resource recommender systems. Those methods suffer from the sparsity problem of the user-item rating matrix, while side information about user and item is seldom used to guide the learning procedure of the MF. MethodTo address those two problems, we proposed a new MOOCs video resource recommender LSMFERLI based on Low-rank and Sparse Matrix Factorization (LSMF) with the guidance of the inter-Entity Relations and intra-entity Latent Information of the students and videos. Firstly, we construct the inter-entity relation matrices and intra-entity latent preference matrix for the students. Secondly, we construct the inter-entity relation matrices and intra-entity affinity matrix for the videos. Lastly, with the guidance of the inter-entity relation and intra-entity affinity matrices of the students and videos, the student-video rating matrix is factorized into a low-rank matrix and a sparse matrix by the alternative iteration optimization scheme. ConclusionsExperimental results on dataset MOOCcube indicate that LSMFERLI outperforms 7 state-of-the-art methods in terms of the HR@K and NDCG@K(K = 5,10,15) indicators increased by an average of 20.6 % and 21.0 %, respectively.

New custom rating for improving recommendation system performance

Recommendation system is currently attracting the interest of many explorers. Various new businesses have surfaced with the rise of online marketing (E-Commerce) in response to Covid-19 pandemic. This phenomenon allows recommendation items through a system called Collaborative Filtering (CF), aiming to improve shopping experience of users. Typically, the effectiveness of CF relies on the precise identification of similar profile users by similarity algorithms. Traditional similarity measures are based on the user-item rating matrix. Approximately, four custom ratings (CR) were used along with a new rating formula, termed New Custom Rating (NCR), derived from the popularity of users and items in addition to the original rating. Specifically, NCR optimized recommendation system performance by using the popularity of users and items to determine new ratings value, rather than solely relying on the original rating. Additionally, the formulas improved the representativeness of the new rating values and the accuracy of similarity algorithm calculations. Consequently, the increased accuracy of recommendation system was achieved. The implementation of NCR across four CR algorithms and recommendation system using five public datasets was examined. Consequently, the experimental results showed that NCR significantly increased recommendation system accuracy, as evidenced by reductions in RMSE, MSE, and MAE as well as increasing FCP and Hit Rate. Moreover, by combining the popularity of users and items into rating calculations, NCR improved the accuracy of various recommendation system algorithms reducing RMSE, MSE, and MAE up to 62.10%, 53.62%, 65.97%, respectively, while also increasing FCP and Hit Rate up to 11.89% and 31.42%, respectively.

Explainable recommender system directed by reconstructed explanatory factors and multi‐modal matrix factorization

SummaryMatrix factorization (MF)‐based recommender systems (RSs) as black‐box models fail to provide explanations for the recommended items. While some models attain a degree of explainability by integrating neighborhood algorithms, which compute explainability based on the preferences of proximate users, they overlook the contribution of the subjective preferences of the target user to enhancing model explainability, resulting in suboptimal model explainability. To address this problem, an explainable RS directed by reconstructed explanatory factors and multi‐modal matrix factorization (ERS‐REFMMF) is proposed. By integrating users' subjective sentiment and preference features into the rating matrix to form a multi‐modal matrix, ERS‐REFMMF utilizes the Funk‐singular value decomposition method at the foundational layer to decompose the multi‐modal matrix and generate a candidate item set. At the upper layer, explainability is constructed based on the target user's subjective preferences and latent features derived from MF, and the final recommended list is optimized for accuracy, diversity, novelty, and explainability through multi‐objective optimization algorithms. ERS‐REFMMF models around users' explicit preferences and latent associations, reconstructs explainability with hybrid factors, and enhances overall performance through a many‐objective optimization algorithm. Experimental results on real datasets demonstrate that the proposed model is competitive in both phases compared to existing recommendation methods.

Application of KL distance-based intelligent recommendation method to fund recommendation for users with investment behavior in Asia Region

Over the past decade, the spectacular growth of many economies in Asia has amazed the economics profession and Asian people's quality of life has been significantly improved. Therefore, people's savings are increasing. The booming financial market attracts more people to invest in funds for its relatively low risk and high returns. This research first analyzed the widely used recommendation algorithms. Then it adopted the K-medoids clustering algorithm based on KL divergence to make recommendations. The recommendations were also based on the user-item rating matrix. First, the improved Kullback-Leibler distance was used to calculate the distance and similarity between the funds. Next, the data set of n funds were split by the K-medoids clustering technique into k clusters to obtain funds that were similar to the target fund. Finally, the prediction accuracy of the KL-KM recommendation algorithm was compared with those of traditional recommendation algorithms. When the number of recommendations changed from 1 to 5, the average minimum KL distance varied from 0.16, 0.22, 0.29, 0.31, to 0.39. The average KL similarity is 0.85, 0.83, 0.79, 0.74, and 0.72, respectively, and the average absolute error is 0.83, 0.82, 0.82, 0.83, and 0.82, respectively. The root mean square error (RMSE) of the proposed algorithm is 0.93, 0.94, 0.93, 0.94 and 0.94, respectively. Therefore, the proposed recommendation model has a better recommendation performance than existing models to meet users' demands. This algorithm generates recommendations of similar funds based on customers' purchase history. Moreover, principal component analysis is used to simplify the large data set of indicator values into a smaller set while still maintaining significant patterns and trends, thereby improving the accuracy and reducing the complexity of the model.

Regionalization-Based Collaborative Filtering: Harnessing Geographical Information in Recommenders

Regionalization, also known as spatially constrained clustering , is an unsupervised machine learning technique used to identify and define spatially contiguous regions. In this work, we introduce a methodology to regionalize recommendation systems (RSs) based on a collaborative filtering approach. Two main challenges arise when performing regionalization based on users’ preferences in RSs: (1) unstructured data, as interactions are often scarce and observed on a smaller scale; and (2) the difficulty of evaluation of the quality of the clustering results. To address these challenges, our methodology relies on inductive matrix completion (IMC), a fundamental approach to recover unknown entries of a rating matrix while utilizing region information to extract a region-based feature matrix. With this feature matrix, our method becomes adaptive and seamlessly integrates with various regionalization algorithms to create regionalization candidates. This enables us to derive more accurate recommendations that consider regionalized effects and discover interesting patterns in localized user behavior. We experimentally evaluate our model on synthetic datasets to demonstrate its efficacy in settings where our underlying assumptions are correct. Furthermore, we present a real-world case study illustrating the interpretable information the model can derive in terms of regionalized recommendation relevance.

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

Faced the vast amount of information, choosing the appropriate materials is a prerequisite for effective self-directed learning. The recommendation algorithm is a kind of intelligent technology that can accurately locate the required information which the users care about most. However, many recommendation techniques experience can not be trained adequately in scenarios with small sample data and extremely sparse ratings. Moreover, DLRAs (Deep learning based Recommendation Algorithms) require high hardware support. The probabilistic graph (PG) can effectively represent the implicit complex relations among nodes, but it still has the problem of sparse data sensitivity. Therefore, we propose a Matrix-Factorization-based Probabilistic Graph Model for Recommendation Algorithm (MF-PGMRA): By matrix-factorizing the sparse rating matrix, the users and items are mapped to the user/item spaces, respectively; We employ the inner product to data-enhance and overcome the problems of sparse data and cold start; Then, we build Probabilistic Graph to construct the “user-item” latent spaces and estimate the probability distribution based on expectation maximization (EM), so as to predict the ratings; Finally, we built a library management system with the recommendation module to highlight the benefits of MF-PGMRA for students’ subject learning. According to a questionnaire, we confirmed that the students are satisfied with the system from four aspects of speed, accuracy, usability and convenience, which can confirm that the library management system based on MF-PGMRA can efficiently and accurately recommend suitable materials for students from the huge amount of learning materials to improve students’ self-directed learning efficiency.

Generation of Rating Matrix Based on Rational Behaviors of Users

This paper proposes a method to generate a synthetic rating matrix based on user’s rational behavior, with the aim of generating a large-scale rating matrix at low cost. Collaborative filtering is one of the major techniques for recommender systems, which is widely used because it can recommend items using only a history of ratings given to the items by users. However, collaborative filtering has some problems such as the cold-start problem and the sparsity problem, both of which are caused by the shortage of ratings in a database (rating matrix). This problem is particularly serious for services that have just started operation or do not have a large number of users. The proposed method generates a rating matrix without missing values using users’ rating probabilities, which are obtained from the distribution of their actual ratings. The final synthetic rating matrix is generated after adjusting its sparsity by introducing missing values. The validity of the proposed method is evaluated by comparing the synthetic rating matrix in terms of the similarity of the distribution of several statistics with that of the real data. The synthetic rating matrix is also evaluated by applying it to recommendation to actual users. The experimental results show that the proposed method can generate the synthetic rating matrix that has similar statistics to the real data, and recommendation models trained with the synthetic data achieve comparable recall to that trained with the real data when using the real data as test data. Based on the results of these experiments, this paper also tries to generate the synthetic rating matrix that contains richer information than the real data by increasing the number of users or reducing the sparsity of the rating matrix. The results of these experiments show the possibility that increasing the information contained in a rating matrix could improve recall.

Personal Value-Based User Modeling Without Attribute Evaluation and its Application to Collaborative Filtering

This paper proposes a personal values modeling method that does not require attribute ratings. The proposed method is applied to memory-based and model-based collaborative filtering (CF) to demonstrate its effectiveness. A recent trend in CF is to introduce additional factors than interaction history. A rate matching rate (RMRate) has been proposed for modeling user’s personal values, and it has been shown to be effective in increasing diversity and recommending niche (long-tail or unpopular) items. However, RMRate needs an attribute-level evaluations in addition to rating (total evaluation) to items, which limits its applicability. To obtain users’ personal values model only from a rating matrix, this paper defines users’ personal values as their tendency to select popular/unpopular items and reputable/unreputable items. Ten attributes are proposed to model user’s personal values, all of which can be calculated from a rating matrix without additional information. Experimental results on four datasets show that the proposed attributes have different characteristics from the RMRate, and can improve precision, recall, and normalized discounted cumulative gain of memory-based CF and factorization machines. It is also shown that the proposed modeling method is useful for mitigating a cold-start problem.

A Sampling-Based Method for Detecting Data Poisoning Attacks in Recommendation Systems

The recommendation algorithm based on collaborative filtering is vulnerable to data poisoning attacks, wherein attackers can manipulate system output by injecting a large volume of fake rating data. To address this issue, it is essential to investigate methods for detecting systematically injected poisoning data within the rating matrix. Since attackers often inject a significant quantity of poisoning data in a short period to achieve their desired impact, these data may exhibit spatial proximity. In other words, poisoning data may be concentrated in adjacent rows of the rating matrix. This paper capitalizes on the proximity characteristics of poisoning data in the rating matrix and introduces a sampling-based method for detecting data poisoning attacks. First, we designed a rating matrix sampling method specifically for detecting poisoning data. By sampling differences obtained from the original rating matrix, it is possible to infer the presence of poisoning attacks and effectively discard poisoning data. Second, we developed a method for pinpointing malicious data based on the distance of rating vectors. Through distance calculations, we can accurately identify the positions of malicious data. After that, we validated the method on three real-world datasets. The results demonstrate the effectiveness of our method in identifying malicious data within the rating matrix.

Assessment of self-perceived risk and risk rating among chemical sprayers in selected tea plantations in South India

Introduction: Chemical sprayers of the tea plantation industry perceive various degrees of risk involved in their daily work. The objective of the study was to assess self-perceived risk and to rate these risks among the pesticide sprayers working in selected tea plantations in South India. Methods: A cross-sectional study was conducted among 290 chemical sprayers in six selected tea plantations in South India from September to October 2018 after approval from the Institutional Ethics Committee and permission from the plantations. Data was collected by structured interview schedule with the chemical sprayers and key informant interviews were conducted with their supervisors. The risks perceived by the sprayers were rated and expressed using the Risk Rating Matrix. Results: The mean age of the participants was 45.6±8.5 years and all of them were males. The most common risks encountered were leech bites (76%), other insect bites (58%) and bruises (46%). Chemical spills, splashes, slips, falls and backaches were considered as minor hazards in risk rating. Animal attacks and falls from trees were assigned the highest risk rating scores. Conclusion: Insect bite was the most common risk perceived and animal attacks attained the highest risk score. Regular supervision and monitoring of work-related risk factors can help in the reduction of common injuries thereby ensuring safety at the workplace.

INCORPORATING SUBJECTIVE FUNCTION IN A USER-BASED COLLABORATIVE FILTERING RECOMMENDER SYSTEM

The existing recommender systems faced a lot of challenges as a result of data sparsity problem, which decreases the recommendations accuracy to the target audience. Though there are numerous studies conducted to reduce the data sparsity problem. However, these studies fail to consider the subjectivity of human beings’ ratings based on lenient and stringent, which lead to unsuitable recommendations. Similarly, it is unable to compute the similarity between pair of users when the ratings among them did not overlap leading to inaccurate recommendations. This work is intended to enhance the adaptability of the system to sparse data by proposing a User Based Collaborative Filtering Recommender System incorporated with data refinement method that utilised a subjective function to identify lenient and stringent ratings in a rating matrix. The function identifies the user’s lenient and stringent ratings to take care of human subjectivity with regard to rating of the items. It also employed a subjective similarity function that used the subjective function for computing the similarity between users. The top similar neighbors obtained from the similarity computations are used to perform prediction for the target user. The experimental results show that compared with other representative systems the proposed system has better recommendations quality.

Kernelized Deep Learning for Matrix Factorization Recommendation System Using Explicit and Implicit Information.

In the current matrix factorization recommendation approaches, the item and the user latent factor vectors are with the same dimension. Thus, the linear dot product is used as the interactive function between the user and the item to predict the ratings. However, the relationship between real users and items is not entirely linear and the existing recommendation model of matrix factorization faces the challenge of data sparsity. To this end, we propose a kernelized deep neural network recommendation model in this article. First, we encode the explicit user-item rating matrix in the form of column vectors and project them to higher dimensions to facilitate the simulation of nonlinear user-item interaction for enhancing the connection between users and items. Second, the algorithm of association rules is used to mine the implicit relation between users and items, rather than simple feature extraction of users or items, for improving the recommendation performance when the datasets are sparse. Third, through the autoencoder and kernelized network processing, the implicit data are connected with the explicit data by the multilayer perceptron network for iterative training instead of doing simple linear weighted summation. Finally, the predicted rating is output through the hidden layer. Extensive experiments were conducted on four public datasets in comparison with several existing well-known methods. The experimental results indicated that our proposed method has obtained improved performance in data sparsity and prediction accuracy.

Mining and Application of Digital Health Elements in Higher Education Student Management and Education

Abstract This study explores the mining and application of digital health elements in higher education student management and education, refining student user profiles through data mining techniques and applying them to student management and education to improve the accuracy and effectiveness of education management. The basic process of data mining, including data cleaning, integration, selection, transformation, mining, pattern evaluation and knowledge representation, was first carried out in the study. Then, a clustering recommendation algorithm based on user characteristics was designed to construct a clustering model of user interest preferences by calculating the distance between user attributes and filling the rating matrix using rating time and item type. Then, the study constructed a student user profiling system and analyzed techniques such as fuzzy C-mean clustering, association rule algorithm and user-based collaborative filtering. The study results show that applying digital health elements in student management and education helps identify potential association patterns of students’ mental health problems. For example, in the W vocational school case study, the association rule algorithm found that the sense of learning stress and compulsion were the most frequent combinations of psychological problems among students, with support levels of 0.7451 and 0.6518, respectively. In addition, the study evaluated the effects of educational management interventions on the mental health of higher vocational students, and found that, after adopting student user profiles for educational management interventions, the experimental class students’ mental health scores for each variable were 0.602 to 1.113 points higher than those of the control class, which significantly improved the students’ psychological quality.