Intelligent Recommendation System Research Articles

Research and Application of Edge Computing and Deep Learning in a Recommender System

Recommendation systems play a pivotal role in improving product competitiveness. Traditional recommendation models predominantly use centralized feature processing to operate, leading to issues such as excessive resource consumption and low real-time recommendation concurrency. This paper introduces a recommendation model founded on deep learning, incorporating edge computing and knowledge distillation to address these challenges. Recognizing the intricate relationship between the accuracy of deep learning algorithms and their complexity, our model employs knowledge distillation to compress deep learning. Teacher–student models were initially chosen and constructed in the cloud, focusing on developing structurally complex teacher models that incorporate passenger and production characteristics. The knowledge acquired from these models was then transferred to a student model, characterized by weaker learning capabilities and a simpler structure, facilitating the compression and acceleration of an intelligent ranking model. Following this, the student model underwent segmentation, and certain computational tasks were shifted to end devices, aligning with edge computing principles. This collaborative approach between the cloud and end devices enabled the realization of an intelligent ranking for product listings. Finally, a random selection of the passengers’ travel records from the last five years was taken to test the accuracy and performance of the proposed model, as well as to validate the intelligent ranking of the remaining tickets. The results indicate that, on the one hand, an intelligent recommendation system based on knowledge distillation and edge computing successfully achieved the concurrency and timeliness of the existing remaining ticket queries. Simultaneously, it guaranteed a certain level of accuracy, and reduced computing resource and traffic load on the cloud, showcasing its potential applicability in highly concurrent recommendation service scenarios.

An intelligent recommender system for tool selection in conventional machining

ABSTRACT Cutting tool manufacturers face a tough challenge in developing custom solutions for specific customer requirements. Several trials are required, encompassing the selection of materials, tool configuration parameters, manufacturing of tools and testing under target conditions to arrive at an acceptable solution. In this work, the authors present a recommender system that utilizes a hierarchical deep learning-based machine learning model, handcrafted using domain knowledge, to predict top N tool configurations for a given target requirement with a probability score. The authors also discuss methods for data augmentation to deal with limited data as well as a probabilistic approach to predict the top N tool configurations from the trained models. The proposed system is applied to a case of centerless cylindrical grinding wheel selection problem. The outcomes indicate an overall accuracy of 92.4% for single best-fit specification, with 100% within the top five recommendations for past designs. Some of the alternatives proposed by the model are observed to be potentially superior to what was chosen earlier by experts. Without the selection hierarchy, a deep learning model achieved a single best-fit accuracy of 83.5% and the probabilistic model achieved a top-five recommendation accuracy of 89.8%, highlighting the merit of the hierarchical approach.

An intelligent recommendation system in e-commerce using ensemble learning

An intelligent recommendation system in e-commerce using ensemble learning

Usability Evaluation of a Knowledge Graph-Based Dementia Care Intelligent Recommender System: Mixed Methods Study.

Knowledge graph-based recommender systems offer the possibility of meeting the personalized needs of people with dementia and their caregivers. However, the usability of such a recommender system remains unknown. This study aimed to evaluate the usability of a knowledge graph-based dementia care intelligent recommender system (DCIRS). We used a convergent mixed methods design to conduct the usability evaluation, including the collection of quantitative and qualitative data. Participants were recruited through social media advertisements. After 2 weeks of DCIRS use, feedback was collected with the Computer System Usability Questionnaire and semistructured interviews. Descriptive statistics were used to describe sociodemographic characteristics and questionnaire scores. Qualitative data were analyzed systematically using inductive thematic analysis. A total of 56 caregivers were recruited. Quantitative data suggested that the DCIRS was easy for caregivers to use, and the mean questionnaire score was 2.14. Qualitative data showed that caregivers generally believed that the content of the DCIRS was professional, easy to understand, and instructive, and could meet users' personalized needs; they were willing to continue to use it. However, the DCIRS also had some shortcomings. Functions that enable interactions between professionals and caregivers and that provide caregiver support and resource recommendations might be added to improve the system's usability. The recommender system provides a solution to meet the personalized needs of people with dementia and their caregivers and has the potential to substantially improve health outcomes. The next step will be to optimize and update the recommender system based on caregivers' suggestions and evaluate the effect of the application.

Selective dissemination of information (SDI) in the age of artificial intelligence (AI)

Purpose In this study, the authors seek to introduce ways that show that in the age of artificial intelligence (AI), selective dissemination of information (SDI) performance can be greatly enhanced by leveraging AI technologies and algorithms. Design/methodology/approach AI holds significant potential for the SDI. In the age of AI, SDI can be greatly enhanced by leveraging AI technologies and algorithms. The authors discuss SDI technique used to filter and distribute relevant information to stakeholders based on the pertinent modern literature. Findings The following conceptual indicators of AI can be utilized for obtaining a better performance measure of SDI: intelligent recommendation systems, natural language processing, automated content classification, contextual understanding, intelligent alert systems, real-time information updates, intelligent alert systems, real-time information updates, adaptive learning, content summarization and synthesis. Originality/value The authors propose the general framework in which AI can greatly enhance the performance of SDI but also emphasize that there are challenges to consider. These include ensuring data privacy, avoiding algorithmic biases, ensuring transparency and accountability of AI systems and addressing concerns related to information overload.

Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning

Intelligent recommendation system of the injection molding process parameters based on CAE simulation, process window, and machine learning

L’impact de la prise en charge des couches sémantiques sur la recherche d'information touristique : Cas des établissements et services touristiques digitalisés au Maroc

Tourism is an economic and a wealth-creating pillar that involves several volatile actors that are rich in dynamic, complex and heterogeneous information (Benckendorff et al., 2019). Therein, the continuous increase in the volume of information available on tourism offerings on the web has made user decision making a crucial task. As a result, the use of personalized and intelligent recommendation systems is essential for the satisfaction of tourism consumers (Grün et al., 2017). The objective of this article is to reveal the impact of the deployment of semantic techniques on the quality and accuracy of search results for tourism information, in the case of tourism in Morocco. This will be done through ontologies, which are a key component in the semantic web (Buhalis, 2020). To this end, we first presented the implementation of the semantic web and the use of ontologies in it. Then, we described the basic background of ontologies. Finally, we discussed the existing ontologies in the tourism domain in Morocco. The findings show that, the assumption of ontological conceptualization during the creation of digital content by tourism establishments, as a context-aware knowledge base, ensures the implementation of a semantic correspondence between users' preferences and the characteristics of tourism offers published, thus improving the quality and accuracy of the recommendations according to the user's context (Abbasi-Moud et al., 2022). Nevertheless, the evolution of tourism ontologies following changes in this sector is of major complexity. For this reason, the invention of an automatic approach, based on artificial intelligence techniques, NLP, Machine Learning or Deep Learning, can contribute to an evolutionary, reliable and easier maintenance of tourism ontologies, in the Moroccan context.

Application of intelligent recommendation system based on deep learning algorithm in university library talent training

Application of intelligent recommendation system based on deep learning algorithm in university library talent training

Toward Human–AI Collaboration: A Recommender System to Support CS1 Instructors to Select Problems for Assignments and Exams

Programming online judges (POJs) have been increasingly used in CS1 classes, as they allow students to practice and get quick feedback. For instructors, it is a useful tool for creating assignments and exams. However, selecting problems in POJs is time-consuming. First, problems are generally not organised based on topics covered in the CS1 syllabus. Second, assessing whether problems require similar effort to be completed and map onto the same topic is a subjective and expert-dependent task. The difficulty increases if the instructor must create variations of these assessments, e.g. to avoid plagiarism. Thus, here we research how to support CS1 instructors in the task of selecting problems, to compose one-size-fits-all or personalised assignments/exams. Our solution is to propose a novel intelligent recommender system, based on a fine-grained data-driven analysis of the students' effort on solving problems in the IDE of a POJ system, and automatic detection of topics for CS1 problems, based on problem descriptions. Data collected from 2714 students are processed to support, via our AI-method recommendations, the instructors' decision-making process. We evaluated our method against the state of the art, in a simple blind experiment with CS1 instructors (N = 35). Results show that our recommendations are 88% accurate, surpassing our baseline ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$p&lt; 0.05$</tex-math></inline-formula> ). Finally, our work paves the way for novel POJ smart learning environments, wherein instructors define learning tasks (assignments/exams) supported by AI.

Ecosystem of Preliminary Investigation

The paper states that the digital transformation of the investigation procedure requires new, effective tools, including those that accumulate the network capabilities of various law enforcement agencies. One of the leading tools in this area are digital platforms. However, the use of not isolated digital platforms of various bodies, but their collaboration into a full-scale digital ecosystem will have a greater synergistic effect. At the same time, it is stated that the prospective ecosystem under consideration should be composed of digital platforms of all three main types: instrumental, infrastructure and applied digital platforms. The paper discusses the essence of digital ecosystems. Within the ecosystem of the preliminary investigation, the author provides for the allocation of at least three main areas (blocks): criminal procedure, organizational (office work) and forensic. Within the framework of the organizational direction, the author considers issues related to the automation of receiving reports of crimes, solving logistical issues of sending guard services, investigative teams, as well as unmanned devices to the scene, expanding the possibilities of electronic office work, automating the selection of the required legislation and judicial practice. In the procedural direction, the use of elements of an electronic criminal case, remote investigative actions (with the use of not only video conferencing, but also other technologies) within the ecosystem is considered. Much attention is given to the use of electronic evidence. Within the framework of the forensic direction, the author considers it important to analyze open databases and Internet information using big data analysis technologies. It also seems reasonable to pair this ecosystem with the Safe City program with the obligatory use of face recognition systems. Intelligent recommender systems and decision support systems are considered. Attention is also given to the study of virtual traces.

Intelligent Recommendation System Based on the Infusion Algorithms with Deep Learning, Attention Network and Clustering

The creation and use of big data have driven the intelligent development of e-commerce. The information generated in e-commerce provides a good means to analyze the behavior of users. How to use this information to give customer recommendations, improve the accuracy of recommendations and protect information security is a topic worth studying. For improving the accuracy of recommendations, analysis of users and tagging of resources are key. The current popular session recommendation algorithms face many problems, such as user interest drift which is difficult to be handled by these algorithms, thus affecting the recommendation accuracy. Based on these problems, this paper proposes a recommendation model based on deep learning, applies it to the clustering analysis of user tagging system, and designs a personalized recommendation algorithm for the tagging system. The model proposed in this paper can effectively analyze not only the interests exhibited by users in the current session, but also their potential long-term interests. By comparing the different performances of different datasets, the experimental results of this paper show that the proposed algorithmic model in this paper helps to dig the interests of different users, thus improving the quality of the recommendation system.

Intelligent recommendation model of tourist places based on collaborative filtering and user preferences

ABSTRACT Providing personalized recommendation service for users and improving the accuracy of recommendation and user satisfaction are the main research tasks of current travel recommendation systems. The intelligent recommendation model of tourist places requires the algorithm to be able to accurately recommend tourist attractions according to the user’s interests. Big data and artificial intelligence technologies have driven the development of intelligent recommendation systems. However, realistic data are often sparse, and the lack of common rating items among users makes some traditional similarity measures impossible to calculate. In addition, traditional collaborative filtering algorithms ignore the issue of user preferences, which can cause a decrease in recommendation accuracy. To address these issues, this paper analyzes the factors affecting users’ interest preferences in terms of their global and local rating information. The interest preferences of users are calculated by computing the probability distribution of user rating information globally and using Hamming approach degree. A similarity algorithm about user preferences is derived using Jeffries-Matusita distance. The similarity algorithm is effectively combined with the traditional similarity algorithm to propose a model of collaborative filtering recommendation algorithm for tourist attractions based on user preferences under sparse data. The paper aims to improve the accuracy of tourist attraction recommendation systems by incorporating user preferences and addressing the issue of sparse data and the lack of common rating items among users that traditional similarity measures cannot calculate. The experimental results show that the improved algorithm model outperforms the traditional collaborative filtering algorithm and other algorithms. And it also has high accuracy rate on more sparse tourism data set.

Models, Methods and Tools of Planning Human Diet

In this article was taken to consideration an issue of planning a person's diet and nutrition and were determined factors that significantly affect the choice of food. It was covered approaches studies on the basis of which modern systems of recommendations for a balanced diet work. A classification of decision support systems regarding the choice of diet and diet is provided based on the data on the basis of which they provide recommendations. The limitations of the linear combination of the user's choice factors and the elements characterizing his profile are shown when modeling a large number of hidden factors affecting the user's diet. The main focus of the research is directed to the analysis of models and methods used in the development of intelligent systems decision support systems about balanced nutrition. The analysis of the advantages and disadvantages of intelligent recommendation systems showed the relevance of developing a complete system that can provide recommendations taking into account a large number of explicit and implicit factors affecting the user's diet and nutrition on his health. Based on the analysis of the models and methods of artificial intelligence already used in such systems, the perspective of the development of models and methods of machine learning has been substantiated and the field of interest for further research has been formed. These studies are planned to be addressed to the construction of a multilayer system of deep machine learning, which will be able to take into account a large number of factors that depend on a balanced diet and healthy eating of each user.

Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence

Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real-life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI), has received more and more attention. GCN has been widely used in different fields such as image processing, intelligent recommender system, knowledge-based graph, and other areas due to their excellent characteristics in processing non-European spatial data. At the same time, communication networks have also embraced AI technology in recent years, and AI serves as the brain of the future network and realizes the comprehensive intelligence of the future grid. Many complex communication network problems can be abstracted as graph-based optimization problems and solved by GCN, thus overcoming the limitations of traditional methods. This survey briefly describes the definition of graph-based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, analyzes the research status, and gives the future research direction.

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