Intelligent Recommendation System Research Articles

An Intelligent Recommendation System for Automating Academic Advising Based on Curriculum Analysis and Performance Modeling

The explosive increase in educational data and information systems has led to new teaching practices, challenges, and learning processes. To effectively manage and analyze this information, it is crucial to adopt innovative methodologies and techniques. Recommender systems (RSs) offer a solution for advising students and guiding their learning journeys by utilizing statistical methods such as machine learning (ML) and graph analysis to analyze program and student data. This paper introduces an RS for advisors and students that analyzes student records to develop personalized study plans over multiple semesters. The proposed system integrates ideas from graph theory, performance modeling, ML, explainable recommendations, and an intuitive user interface. The system implicitly implements many academic rules through network analysis. Accordingly, a systematic and comprehensive review of different students’ plans was possible using metrics developed in the mathematical graph theory. The proposed system systematically assesses and measures the relevance of a particular student’s study plan. Experiments on datasets collected at the University of Dubai show that the model presented in this study outperforms similar ML-based solutions in terms of different metrics. Typically, up to 86% accuracy and recall have been achieved. Additionally, the lowest mean square regression (MSR) rate of 0.14 has been attained compared to other state-of-the-art regressors.

Optimization of purchasing business process in Moroccan public universities based on COBIT and artificial intelligence techniques

PurposeThe main goal of this research work is the optimization of the purchasing business process in the Moroccan public sector in terms of transparency and budgetary optimization. The authors have worked on the public university as an implementation field.Design/methodology/approachThe design of the research work followed the design science research (DSR) methodology for information systems. DSR is a research paradigm wherein a designer answers questions relevant to human problems through the creation of innovative artifacts, thereby contributing new knowledge to the body of scientific evidence. The authors have adopted a techno-functional approach. The technical part consists of the development of an intelligent recommendation system that supports the choice of optimal information technology (IT) equipment for decision-makers. This intelligent recommendation system relies on a set of functional and business concepts, namely the Moroccan normative laws and Control Objectives for Information and Related Technology's (COBIT) guidelines in information system governance.FindingsThe modeling of business processes in public universities is established using business process model and notation (BPMN) in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature. Implementation of artificial intelligence techniques can bring great value in terms of transparency and fluidity in purchasing business process execution.Research limitations/implicationsBusiness limitations: First, the proposed system was modeled to handle one type products, which are computer-related equipment. Hence, the authors intend to extend the model to other types of products in future works. Conversely, the system proposes optimal purchasing order and assumes that decision makers will rely on this optimal purchasing order to choose between offers. In fact, as a perspective, the authors plan to work on a complete automation of the workflow to also include vendor selection and offer validation. Technical limitations: Natural language processing (NLP) is a widely used sentiment analysis (SA) technique that enabled the authors to validate the proposed system. Even working on samples of datasets, the authors noticed NLP dependency on huge computing power. The authors intend to experiment with learning and knowledge-based SA and assess the' computing power consumption and accuracy of the analysis compared to NLP. Another technical limitation is related to the web scraping technique; in fact, the users' reviews are crucial for the authors' system. To guarantee timeliness and reliable reviews, the system has to look automatically in websites, which confront the authors with the limitations of the web scraping like the permanent changing of website structure and scraping restrictions.Practical implicationsThe modeling of business processes in public universities is established using BPMN in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature.Originality/valueThe adopted techno-functional approach enabled the authors to bring information system governance from a highly abstract level to a practical implementation where the theoretical best practices and guidelines are transformed to a tangible application.

An intelligent recommender system for people who are prone to fatty liver disease

IntroductionConsidering the growth of the outbreak of fatty liver disease, it seems necessary and needed to design and develop an intelligent recommender system in order to provide clinical recommendations and a healthy lifestyle and also to provide a suitable educational and counseling platform. In this regard, the present research was conducted in order to design, develop and evaluate an intelligent recommender system for people who are prone to non-alcoholic fatty liver disease. Materials and methodsThe current research was of the applied-developmental type which was carried out in order to create a non-alcoholic fatty liver intelligent recommender system. In the stage of the knowledge base designing, the selected data elements were placed in the form of tables in the SQL software. In the stage of the system designing, first the physical and functional features of the system were drawn by UML diagram, then coding was done using C# programming language. At the final stage, the usability evaluation of the designed system was assessed using the System Usability Scale (SUS). ResultsThe data requirements and technical capabilities were identified in three areas: demographic, clinical and technical requirements. In order to create a recommender system, the Microsoft SQL Server database and C# programming languages were used in the asp.net environment in a reactive manner. The fatty liver intelligent recommender system designed in this study had an average score of 74.625 in the evaluation of SUS applicability. Therefore, the usability of the fatty liver intelligent recommender system was approved by experts. ConclusionIn this study, through the designed system, the researchers provided a platform to remotely communicate between the patient and the specialist and receive unlimited self-care recommendations. Investigating the effectiveness of using the current recommender system to advance treatment goals and follow up on the patient's condition is suggested for future studies.

The Use of AI in E-Learning Recommender Systems: A Comprehensive Survey

Nowadays, Artificial Intelligence (AI) technology, especially, machine learning has received significant attention from many researchers and professionals in several domains. For instance, this technology has revolutionized industrials sectors, such as e-commerce, health, manufacturing, and entertainment, as well as, the educational sector. In this context, during the pandemic, educational institutions around the globe recognized the benefits of adopting remote learning and teaching based on many e-learning platforms. Hence, deploying virtual classrooms is not that easy, due to many constraints, such as time, technological cost, lack of interactivity of learners, and the unsuitability of the learner's cognitive abilities and learning style. To this end, understanding the learner's style and providing adapted and personalized content are the main concerns of today's e-learning systems. To this end, integrating efficient recommender systems into online learning environment is required in order to consider student's behaviors and preferences when recommending various learning materials. Thus, intelligent recommender systems are able to provide a personalized learning path in which the system adapts to the student's learning requirements and abilities. This review discusses the different applications, methods, and challenges of AI-based recommendation systems used to assist different stockholders in an online learning environment.

Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

The digital technologies that run based on users’ content provide a platform for users to help air their opinions on various aspects of a particular subject or product. The recommendation agents play a crucial role in personalizing the needs of individual users. Therefore, it is essential to improve the user experience. The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites. In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation. A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation. The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users. However, the key issue is how contextual information is used to create good and intelligent recommender systems. This paper proposes an Artificial Neural Network (ANN) to achieve contextual recommendations based on user-generated reviews. The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS. Thus, the most appropriate contexts in which a user should choose an item or service are achieved. This work converts every label set into a Multi-Label Classification (MLC) problem to enhance recommendations. Experimental results show that the proposed ANN performs better in the Binary Relevance (BR) Instance-Based Classifier, the BR Decision Tree, and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset. Furthermore, the accuracy of the proposed ANN achieves better results by 1.1% to 6.1% compared to other existing methods.

Retracted: Research and Implementation of Electronic Commerce Intelligent Recommendation System Based on the Fuzzy Rough Set and Improved Cellular Algorithm

Retracted: Research and Implementation of Electronic Commerce Intelligent Recommendation System Based on the Fuzzy Rough Set and Improved Cellular Algorithm

Retracted: Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

Retracted: Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

SNPERS: A Physical Exercise Recommendation System Integrating Statistical Principles and Natural Language Processing

As chronic diseases such as cardiovascular diseases are prevalent and progressively more common in young people, more and more college students are paying attention to exercising, even though they are busy studying. However, some college students are unmindful of their physique and their bodies’ targeted exercise. The exercise they do is either extensive but not refined or too homogeneous. We conducted a statistical analysis of 18,101 college students’ physical examination results. We found that students who exercise regularly but still did not achieve satisfactory results in one or more physical examination items had often exercised in the two unscientific ways mentioned above. This paper presents an intelligent recommendation system that integrates statistical principles and natural language processing, which improves traditional recommendation systems and could provide suitable and targeted exercise suggestions for college students. The R2 increased by about 27.72%.

Deepsentimodels: A Novel Hybrid Deep Learning Model for an Effective Analysis of Ensembled Sentiments in E-Commerce and S-Commerce Platforms

Social networks and media have gradually grabbed significant time from people’s lives to share and communicate information. Moreover, these social platforms also act as an emotional catalyst for expressing their feeling and views on different products, movies, and even national policies. Thus, it helps to understand people’s opinions and predict their behaviors in social networks, where it helps the entrepreneur improve their products or services. It is considered the most efficient method to understand the customer’s view on their product better. Since entrepreneurs are increasing exponentially, an intelligent system is required to understand people’s views on their products. Also, people use informal language to express their sentiments; it has become a daunting task to extract the sentiments that reflect the people’s attitude and feedback about the product. Therefore, this article suggests a novel hybrid deep learning system, consisting of bi-convolutional networks (B-CNN) and spotted hyena optimized long short term memory (SHOLSTM), for improved understanding of people’s feelings and the construction of intelligent recommendation systems for entrepreneurs. Many experiments have been conducted utilizing various datasets compared to other current hybrid deep learning methods, including long short-term memory (LSTM), convolutional neural networks (CNN), BIGRU, and attention classifiers. Accuracy is achieved as 99.4%, 99% precision, 99.2% recall, 99.2% F1-score is achieved, which suits these algorithms best for implementing the intelligent recommendation systems based on the different ensembled sentiments.

Intelligent recommendation system based on decision model of archive translation tasks

How to recruit, test, and train the intelligent recommendation system users, and how to assign the archive translation tasks to all intelligent recommendation system users according to the intelligent matching principles are still a problem that needs to be solved. With the help of proper names and terms in China's Imperial Maritime Customs archives, this manuscript aims to solve the problem. When the corresponding translation, domain or attributes of a proper name or term is known, it will be easier for some archive translation tasks to be completed, and the adaptive archive intelligent recommendation system will also improve the efficiency of intelligent recommendation quality of archive translation tasks. These related domains or attributes are different labels of these archives. To put it simply, multi-label classification means that the same instance can have multiple labels or be labelled into multiple categories, which is called multi-label classification. With the multi-label classification, archives can be classified into different categories, such as the trade archives, preventive archives, personnel archives, etc. The system users are divided into different professional domains by some tests, for instance, system users who are good at economic knowledge and users who have higher language skills. With these labels, the intelligent recommendation system can make the intelligent match between the archives and system users, so as to improve the efficiency and quality of intelligent archive translation tasks. In this manuscript, through multi-label classification, the intelligent recommendation system can realize the intelligent allocation of archive translation tasks to the system users. The intelligent allocation is realized through the construction of intelligent control model, and verifies that the intelligent recommendation system can improve the performance of task allocation over time without the participation of task issuers.

Graph-embedding-inspired article recommendation model

Intelligent recommendation systems(IRSs) need to face the problems of sparse data and cold start. As one of the intelligent recommendation scenarios, article recommendation has rich and various article information, which is a significant difference from other scenarios. However, the existing methods of article recommender do not fully consider the article information or ignore the implicit correlation between users and articles. Therefore, this paper proposes a Graph-Embedding-inspired Article Recommendation Model (GE-ARM): we capture the “User-Article” correlation feature embeddings by constructing a Bipartite Graph with Resource Allocation (RA-BG); considering the different contributions of the “Title, Abstract” and “Tag, Citation”, design an Attention-based Dual-AutoEncoder, combining with Collaborative Filtering to fully capture the article feature embeddings; and update the correlation and article feature embeddings with Probabilistic Matrix Factorization to perform the final recommendation task. The experimental results highlight that GE-ARM outperforms the other known methods on four datasets.

New Recommendation System Based on Students' Engagement Prediction Using CNN to Optimize E-Learning

The advantage of face-to-face teaching is the teacher's ability to naturally assess the engagement of his students, while adapting his course to their training needs. Whereas, in an online teaching environment, the identification of the learners' engagement becomes an arduous task involving considerable effort on behalf of the teacher negatively impacting the quality of his rendering. Therefore, the authors of this paper focus on the development of a new intelligent recommendation system for the optimization of the quality of online learning and teaching. This system provides suggestions to teachers or students based on their state of engagement. Moreover, the system developed is based on a deep learning model, the convolutional neural network, which has confirmed its reliability with an accuracy of 88%. Finally, the future scalability of the recommendation system is ensured through the introduction of a new learning indicator that can be used for further predictions.

A privacy‐preserving recommendation method with clustering and locality‐sensitive hashing

AbstractNowadays, there is a significant increase in information, resulting in information overload. Recommendation systems have been widely adopted, and they can help users find information relevant to their interests. However, a malicious attacker can infer users' private information via recommendations. To solve problems of data sparseness, enormous high‐dimensional data, the cold start problem and privacy protection in an intelligent recommender system, this study proposes a privacy‐preserving collaborative filtering recommendation method with clustering and locality‐sensitive hashing. First, we cluster users according to their characteristic information to obtain sub‐rating matrices. We use the latent factor model to predict and fill in the missing ratings in those matrices. Second, we combine the sub‐rating matrices into a complete rating matrix, subsequently, we obtained the neighbors of the target user by analyzing the similarity of the users. We use a locality‐sensitive hashing algorithm to reduce the dimensionality of the user rating data and build an index that could quickly obtain the neighbors of the target user. Finally, we predict the target user's ratings and provide recommendations to the target user. Through experiments, our study shows that our method can deal with the problems of data sparseness and cold start problems well and the accuracy of the intelligent recommendation system has been improved. In addition, we use hash techniques to search for the neighbors, which effectively protects the privacy of the user.

Improvement of Business Analysis Method of E-Commerce System from the Perspective of Intelligent Recommendation System

In recent years, with the continuous development of the country’s Internet platforms, China has gradually entered the e-commerce era of national online shopping, and more and more e-commerce platforms and stores have adopted intelligent recommendation systems to increase transaction rates. However, it is not easy for consumers to filter out the products they want from a large amount of information. The emergence of intelligent recommendation systems provides great convenience for people to screen out personalized products that meet their own characteristics. However, the algorithms used in traditional recommendation technology focus on the single-computer environment and do not consider the performance of the recommendation method when distributed parallel processing is required in the big data environment, so it cannot meet the personalized needs of users in the big data environment. Aiming at the new requirements for the development of e-commerce intelligent recommendation technology in the big data environment, this paper uses the big data processing technology based on cloud computing and focuses on the realization technology of the e-commerce intelligent recommendation algorithm and the comprehensive evaluation method of the recommendation system in the big data environment. A prototype system of personalized intelligent recommendation based on cloud computing has been developed, which is of great importance to meet the needs of e-commerce personalized intelligent recommendation in the big data environment, improve the effectiveness, scale, and real-time performance of the personalized intelligent recommendation system, and improve the level of personalized precision marketing., which is of theoretical significance and economic value.

Design and Application of Marketing Intelligent Platform Based on Big Data Technology

The progress of big data technology has promoted the development of intelligent marketing for education companies. The online course selection system on the official website of the education company can provide users with online course selection and purchase services. The early marketing conversion rate remained below 0.09% throughout the year. After the analysis of the marketing department, it is found that there are some problems on the official website, such as a low marketing conversion rate. To improve the marketing conversion rate of the official website, the marketing department puts forward business needs. The experiential marketing strategy has been formulated, and it is planned to implement experiential course purchase and secondary marketing on the official website. The IT R & D department has established a big data marketing intelligent recommendation system project to meet the business needs put forward by the marketing department. Firstly, use the big data real-time computing technology to collect the user behavior information of users screening courses on the official website. Real time analysis of user behavior information can predict the courses given to users, which is conducive to the purchase of courses. Then, use the big data machine learning technology to integrate the marketing activity opportunity data in each marketing activity opportunity management system and establish a user portrait. For users who have participated in online or offline marketing activities but have not purchased courses, offline predict the courses and classes that can be recommended to users to carry out secondary marketing for them. Finally, the information of recommendable courses and classes predicted in real time and offline is displayed in the display area of the official website, which makes the marketing conversion rate of the official website reach 0.20%, which is 125% higher than that of the original official website.

Learning Analytics and Computerized Formative Assessments: An Application of Dijkstra’s Shortest Path Algorithm for Personalized Test Scheduling

The use of computerized formative assessments in K–12 classrooms has yielded valuable data that can be utilized by learning analytics (LA) systems to produce actionable insights for teachers and other school-based professionals. For example, LA systems utilizing computerized formative assessments can be used for monitoring students’ progress in reading and identifying struggling readers. Using such LA systems, teachers can also determine whether progress is adequate as the student works towards their instructional goal. However, due to the lack of guidelines on the timing, number, and frequency of computerized formative assessments, teachers often follow a one-size-fits-all approach by testing all students together on pre-determined dates. This approach leads to a rigid test scheduling that ignores the pace at which students improve their reading skills. In some cases, the consequence is testing that yields little to no useful data, while increasing the amount of instructional time that students miss. In this study, we propose an intelligent recommender system (IRS) based on Dijkstra’s shortest path algorithm that can produce an optimal assessment schedule for each student based on their reading progress throughout the school year. We demonstrated the feasibility of the IRS using real data from a large sample of students in grade two (n = 668,324) and grade four (n = 727,147) who participated in a series of computerized reading assessments. Also, we conducted a Monte Carlo simulation study to evaluate the performance of the IRS in the presence of unusual growth trajectories in reading (e.g., negative growth, no growth, and plateau). Our results showed that the IRS could reduce the number of test administrations required at both grade levels by eliminating test administrations in which students’ reading growth did not change substantially. In addition, the simulation results indicated that the IRS could yield robust results with meaningful recommendations under relatively extreme growth trajectories. Implications for the use of recommender systems in K–12 education and recommendations for future research are discussed.

Intelligent Recommendation System for English Vocabulary Learning – Based on Crowdsensing

Abstract With the advent of the artificial intelligence era, the traditional methods of online learning can no longer meet people’s personalised needs. Take the English vocabulary memory APP, which is widely used in the market of learning software, as an example. At present, there are various factors that make it difficult for learners to improve their learning efficiency, reduce their learning enthusiasm and enhance their learning effects. Therefore, in this paper, the application of machine learning algorithm in intelligent recommendation of English vocabulary learning is studied, and an intelligent recommendation system for English vocabulary learning based on crowdsensing is established. In its functional module, machine learning algorithms such as clustering algorithm, word vector training and collaborative filtering recommendation are used to realise the intelligent recommendation in the system. Finally, accurate resources of recommendation for user groups with different needs, interest characteristics and abilities improves the efficiency of English vocabulary learning.

An Adaptive Recommendation Algorithm of Intelligent Clothing Design Elements Based on Large Database

In the recent years, the developmental speed of intelligent technology continues to accelerate, and the research on the actual needs of users is also in depth. From the current situation of the clothing industry, how to combine artificial intelligence (AI) technology with clothing fashion has become the focus of customer’s attention. The application of intelligent clothing matching recommendation system (online) can effectively meet the needs of customers in dressing matching, so as to save a lot of time and energy (offline). With the maturity of artificial intelligence, machine learning, and other emerging computational technologies, the intelligent clothing matching system has laid a solid foundation. In this paper, several intelligent clothing matching recommendation systems that have been applied at present are deeply analyzed. Moreover, the basic algorithms and key technologies are elaborated in detail. In addition, the future research direction is found, so that the clothing matching recommendation system can be more personalized, and the comprehensive function is greatly improved in order to bring more ideal benefits.

Research on Product Design Strategy Based on User Preference and Machine Learning Intelligent Recommendation

In the machine learning model, intelligent recommendation system can select valuable information from a lot of data to help users find the products or services they need, which has been more and more widely used in recent years. However, there are still many problems in machine learning recommender systems, such as data sparsity, natural noise, and cold start, which leads to the fact that machine learning recommender systems cannot obtain accurate user preferences. When a project is rated, the quality of the recommendation is greatly affected. In order to solve the problem that the existing recommendation algorithms have poor recommendation results in sparse data sets, this paper proposes a machine learning method for recommendation rating prediction based on user interest concept lattice. Firstly, the nearest neighbors are divided into direct nearest neighbors and indirect nearest neighbors by user interest concept lattice. Then, different methods are used to calculate the similarity between the direct “nearest neighbor” and the target user, and the similarity between the indirect “nearest neighbor” and the target user. Finally, the invisible item score of the target user is calculated by the similarity value. Experiments are carried out on real data sets, and the experimental results show that the CFCNN-CL algorithm and RRP-UI CL algorithm proposed in this paper have high recommendation accuracy and still have good performance in the case of sparse data.

Fabrics Recommendation for Fashion Design by Using Fuzzy Logic and Rough Sets

Background: Fabric is one of the key and vital design factors in fashion design. However, selection of relevant fabrics is rather complex for designers and managers due to the complexity of criteria at different levels. Introduction: In this paper, we propose a new fabric recommendation model in order to quickly realize fabric selection from non-technical fashion features only and predict fashion features from any fabric technical parameters. This approach is extremely significant for fashion designers who do not completely master fabric technical details. It is also very useful for fabric developers who have no knowledge on fashion markets and fashion consumers. Method: The proposed fabric recommendation model has been built by exploiting designers’ professional knowledge and consumers’ preferences. Concretely, we first use fuzzy sets for formalizing and interpreting measured technical parameters and linguistic sensory properties of fabrics and then model the relation between the technical parameters and sensory properties by using rough sets. Next, we model the relation between fashion themes and sensory properties using fuzzy relations. By combining these two models, we establish a hybrid model characterizing the relation between fashion themes and technical parameters. Result: The proposed model has been validated through a real fabric recommendation case for designer’s specific requirements. We can find that the proposed model is efficient since the averaged value of prediction errors is 8.57%, which does not exceed 10% (generally considered as allowable range of human perception error). Conclusion: The proposed model will constitute one important component for establishing an intelligent recommender system for garment design, enabling to support innovations in textile/apparel industry in terms of mass customization and e-shopping.