Intelligent Recommendation Research Articles

Intelligent recommendation algorithm for social networks based on improving a generalized regression neural network

<p>In recent years, the vigorous development of the Internet has led to the exponential growth of network information. The recommendation system can analyze the potential preferences of users according to their historical behavior data and provide personalized recommendations for users. In this study, the social network model was used for modeling, and the recommendation model was improved based on variational modal decomposition and the whale optimization algorithm. The generalized regression neural network structure and joint probability density function were used for sequencing and optimization, and then the genetic bat population optimization algorithm was used to solve the proposed algorithm. An intelligent recommendation algorithm for social networks based on improved generalized regression neural networks (RA-GNN) was proposed. In this study, three kinds of social network data sets obtained by real crawlers were used to solve the proposed RA-GNN algorithm in the real social network data environment. The experimental results showed that the RA-GNN algorithm proposed in this paper could implement efficient and accurate recommendations for social network information.</p>

An intelligent recommendation method of remote ideological and political education resources based on user clustering

An intelligent recommendation method of remote ideological and political education resources based on user clustering

An intelligent Recommendation Method of Remote Ideological and Political Education Resources Based on User Clustering

An intelligent Recommendation Method of Remote Ideological and Political Education Resources Based on User Clustering

Natural Language Processing Based Classification of Publication Data

Abstract: In the ever-expanding landscape of scholarly publications, the need for efficient and accurate methods of classifying and organizing vast amounts of information has become imperative. This research explores the application of Natural Language Processing (NLP) techniques to enhance the classification of publication data. By leveraging advanced linguistic and machine learning approaches, we aim to automate and optimize the categorization of diverse publications, thereby facilitating streamlined access to relevant knowledge.The proposed methodology involves the extraction of key features from textual content, such as abstracts, titles, and keywords, using state-of-the-art NLP algorithms. These features serve as input for a robust classification model that is trained on a diverse dataset of publications spanning various domains. The model's performance is fine-tuned through iterative processes, ensuring adaptability to the nuances and evolving trends within different research fields. Furthermore, we explore the integration of domain-specific ontologies and semantic analysis to enhance the precision and granularity of classification. This allows for a more nuanced understanding of the relationships between publications, enabling users to navigate through knowledge landscapes with increased contextual relevance.The study's significance lies in its potential to revolutionize the way researchers, academics, and professionals access and organize vast amounts of information. The proposed NLP-based classification system not only promises efficiency in information retrieval but also lays the groundwork for developing intelligent recommendation systems tailored to individual user preferences and research interests.Ultimately, this research contributes to the evolving field of information science by presenting a novel approach to publication data classification that aligns with the accelerating pace of information creation and dissemination in today's knowledge-driven society

A Design and Intelligent Recommendation Method for Ballistic Missile Early Warning Operation Plan

Ballistic missile attack-defense confrontation is a typical system-of-systems confrontation process. Ballistic missile early warning operations have the characteristics of high time-sensitivity, high complexity and high dynamic. It is impossible to deal with complex battlefield situations at the moment of decision. It is necessary to develop a detection plan in advance for possible battlefield situations, and intelligently recommend the optimal detection plan for the commander to make decisions based on the battlefield situation. The ballistic missile early warning detection plan is designed from three aspects: the design of search screen parameters, the determination of search data rate, and the allocation strategy of search resources in different airspace; Establish the evaluation indicator system and evaluation model of the ballistic missile early warning detection plan, evaluate and optimize multiple detection plans, and automatically recommend them to the commander according to the advantages and disadvantages of the operational effectiveness, connect the auxiliary decision-making chain of “design evaluation recommendation decision”, providing support for the intelligent decision-making of the ballistic missile early warning operation.

Retracted: Intelligent Recommendation System of Dance Art Video Resources Based on the Wireless Network

Retracted: Intelligent Recommendation System of Dance Art Video Resources Based on the Wireless Network

Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches

Machine learning (ML) techniques hold promise for innovating teacher preparation and development programs. However, the current state of research leveraging artificial intelligence in teacher-focused contexts remains unclear. This study undertook a systematic bibliometric analysis to characterize the emerging domain investigating ML applications for enhancing teacher effectiveness. Using the bibliographic R tool Bibliometrix, metadata of 740 English-language articles published during 2019-2023 extracted from Web of Science educational databases were examined to determine performance metrics, science mapping, citation networks, and research trends situating at the intersection of ML and teacher education. Document growth averaged 39.57% annually, with collaborations involving 87% of publications and 21.62% engaging international co-authorships. The USA led productivity metrics, though opportunities exist to expand geographical diversity. Analyses revealed research activity presently concentrates around employing ML for student analytics, assessment frameworks, and online learning environments. Highly cited works dealt with ML systems for evaluation and competency modeling of teachers rather than directly supporting pedagogical practice. Significant gaps persist exploring intelligent recommendation engines and affective computing chatbots tailored to teachers’ dynamic training needs and emotional responses. This bibliometric review synthesizes the contours and trends in investigating ML applications for augmenting teachers’ capabilities. Findings inform stakeholders to mobilize efforts strategically advancing this domain for enriching classrooms.

Retracted: Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

Retracted: Intelligent Recommendation Algorithm of Multimedia English Distance Education Resources Based on User Model

A Study on the Continued Use of KnowledgeBased APP Users in Guizhou Based on the Perspective of Perceived Value

The wide variety of products offered by online sales platforms makes choosing the correct item a challenging task. The expertise and knowledge of customers in such product assortments can vary widely. Therefore, there is a need for intelligent recommender systems that provide personalized dialogues that support customers during the product selection process. The three research objectives are: to explore the factors in continuing for using knowledge-based apps; to identify the relationship between influencing factors and sustainable usage intention; to search for the countermeasures and suggestions for the continued development of applications user policies.

A Study on the Continued Use of KnowledgeBased APP Users in Guizhou Based on the Perspective of Perceived Value

The wide variety of products offered by online sales platforms makes choosing the correct item a challenging task. The expertise and knowledge of customers in such product assortments can vary widely. Therefore, there is a need for intelligent recommender systems that provide personalized dialogues that support customers during the product selection process. The three research objectives are: to explore the factors in continuing for using knowledge-based apps; to identify the relationship between influencing factors and sustainable usage intention; to search for the countermeasures and suggestions for the continued development of applications user policies.

Інтелектуальна система ідентифікації рейтингу довіри користувача

The article develops an intelligent system for identifying the user's trust rating, which allows viewing information about contacts that are more aimed at creating trust in the interlocutor or providing information that helps to identify the caller or the person with whom we are trying to contact. The k-nearest neighbours method was chosen to create the recommendation system. The main advantage of using the k-nearest neighbours method is the ability to take into account the unique trust rating of each phone number. It is important not only to find phone numbers with a similar rating, but also to take into account the approximate rating. The k-nearest neighbours method allows you to provide recommendations based on the similarity between phone numbers that have received positive feedback from users with similar preferences. Intelligent recommendation systems can provide phone numbers with similar ratings. When a user requests information about a phone number associated with fraudsters, the system instantly offers low-rated phone numbers that are therefore potential fraudsters.

When Post Hoc Explanation Knocks: Consumer Responses to Explainable AI Recommendations

Artificial intelligence (AI) recommendations are becoming increasingly prevalent, but consumers are often reluctant to trust them, in part due to the “black-box” nature of algorithm-facilitated recommendation agents. Despite the acknowledgment of the vital role of interpretability in consumer trust in AI recommendations, it remains unclear how to effectively increase interpretability perceptions and consequently enhance positive consumer responses. The current research addresses this issue by investigating the effects of the presence and type of post hoc explanations in boosting positive consumer responses to AI recommendations in different decision-making domains. Across four studies, the authors demonstrate that the presence of post hoc explanations increases interpretability perceptions, which in turn fosters positive consumer responses (e.g., trust, purchase intention, and click-through) to AI recommendations. Moreover, they show that the facilitating effect of post hoc explanations is stronger in the utilitarian (vs. hedonic) decision-making domain. Further, explanation type modulates the effectiveness of post hoc explanations such that attribute-based explanations are more effective in enhancing trust in the utilitarian decision-making domain, whereas user-based explanations are more effective in the hedonic decision-making domain.

Intelligent Recommender Systems for Medicine. Particularities and Limitations

Intelligent Recommender Systems for Medicine. Particularities and Limitations

Usability evaluation of a knowledge graph‐based dementia care intelligent recommender system: A mixed‐methods study

AbstractBackgroundKnowledge 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.ObjectiveThis study aimed to evaluate the usability of a knowledge graph‐based dementia care intelligent recommender system (DCIRS).MethodsA convergent mixed‐methods design was employed to conduct the usability evaluation, including the collection of quantitative and qualitative data. Participants were recruited via social media advertisements. After two weeks of DCIRS use, feedback was collected with the Computer System Usability Questionnaire and semi‐structured interviews. Descriptive statistics were used to describe sociodemographic characteristics and questionnaire scores. Qualitative data were analyzed systematically using inductive thematic analysis.ResultsA 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, 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.ConclusionsThe 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.

Developing an Intelligent Recommendation System for Non-Information and Communications Technology Major University Students

Various services and applications based on information and communications technology (ICT) are converging with cultural aspects of historical implementations. At the same time, developing a convergence course for non-ICT majors is becoming increasingly popular in universities. In this paper, we develop an AI application course for non-ICT major university students toward convergence with recommendation systems and Silk Road studies. Based on our five-year research on the martial arts, dance, and play of seven Silk Road countries, we have created and categorized an accessible database for 177 items in those countries. For our convergence course, we measure the similarity between the items for summary and perform collaborative filtering based on alternating least squares (ALS) matrix factorization so that our prototyped intelligent recommendation engine can predict the items in which a user might be interested. The course is designed to teach non-ICT major university students not only historical aspects of the Silk Road but also implementation aspects of recommendation systems with web services.

Analysis of the Teaching Mode Based on the Integration and Mutual Promotion of Cultural and Creative Professional Construction and Curriculum Civics in the Context of Deep Learning

Abstract In this paper, an intelligent fusion teaching system is constructed in the context of deep learning. Firstly, a convolutional neural network is utilized to extract multi-level knowledge point features, and a nonlinear function is combined to accelerate the fusion of knowledge point features. Then, semantic features are integrated into keyword extraction, and the SKE algorithm calculates the semantic contribution value of words, and word centering density, and the words are matched according to the keyness. Finally, multiple regression analysis is used for the dynamic mining of online learning behavior data to get the frequent dynamic metric distribution set of the data so as to realize intelligent recommendations. The results show that 75% of the students believe that the integration of curriculum Civics in the cultural and creative majors can enhance the values and is conducive to the integration and development of Civics education and the construction of cultural and creative majors.

The Construction of English Precision Education Model in Colleges and Universities under the Background of Artificial Intelligence

Abstract This paper first deconstructs the core of the college English precision education model, combines relevant theoretical knowledge, and constructs a college English precision education model based on artificial intelligence. Secondly, the convolutional neural network technology is integrated into the joint probability matrix decomposition model, and the resource recommendation list is generated through the cognitive ability level of students and their individualized needs so as to realize the precise, intelligent recommendation of English education resources. Finally, the performance of the recommendation for teaching English educational resources and the practical effect of the educational model are explored through comparative experiments. The results show that the precision rate and recall rate of the recommendation with a test set of 10% are 0.871 and 0.866, respectively, and the F1 value is 0.88. After the teaching practice, students’ listening ability improved by 0.132, expression ability improved by 0.13, reading ability improved by 0.132, writing ability improved by 0.130, and translation ability improved by 0.128. Based on the paper, the educational model has a positive impact on improving the level of English education in colleges and universities.

Recommendation Systems for e-Shopping: Review of Techniques for Retail and Sustainable Marketing

In recent years, the interest in recommendation systems (RSs) has dramatically increased, as they have become main components of all online stores. The aims of an RS can be multifaceted, related not only to the increase in sales or the convenience of the customer, but may include the promotion of alternative environmentally friendly products or to strengthen policies and campaigns. In addition to accurate suggestions, important aspects of contemporary RSs are therefore to align with the particular marketing goals of the e-shop and with the stances of the targeted audience, ensuring user acceptance, satisfaction, high impact, and achieving sustained usage by customers. The current review focuses on RS related to retail shopping, highlighting recent research efforts towards enhanced e-shops and more efficient sustainable digital marketing and personalized promotion. The reported research was categorized by main approach, key methods, and specialized e-commerce problems addressed, while technological aspects were linked with marketing aspects. The increasing number of papers in the field showed that it has become particularly popular, following the explosive growth in e-commerce and mobile shopping. The problems addressed have expanded beyond the performance of the core algorithms to the business aspects of recommendation, considering user acceptance and impact maximization techniques. Technologies have also shifted from the improvement of classic filtering techniques to complex deep learning architectures, in order to deal with issues such as contextualization, sequence-based methods, and automatic feature extraction from unstructured data. The upcoming goals seem to be even more intelligent recommendations that more precisely adapt not only to users’ explicit needs and hidden desires but also to their personality and sensitivity for more sustainable choices.

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.

Optimization of intelligent recommendation of innovation and entrepreneurship projects based on collaborative filtering algorithm

The advent of the information age has made accurate search for information a challenge. In this paper, we analyze intelligent recommendations for innovative entrepreneurial projects based on collaborative filtering algorithms. Collaborative filtering is one of the most widely used and successful techniques in recommendation systems. In this paper, an interest migration function plus time is introduced to address the shortcomings of traditional collaborative filtering recommendation algorithms. Meanwhile, this paper builds an intelligent recommendation engine system for innovative entrepreneurial projects based on the Hadoop open-source distributed computing framework, sustainable PSCM, and Mahout collaborative filtering recommendation engine technology. This paper uses experiments to test and evaluate the overall performance of the distributed recommendation platform and the improved collaborative filtering recommendation algorithm. It is found that the algorithm outperforms similar algorithms in terms of data volume and coverage of recommended innovation and entrepreneurship projects. This is sufficient to show that the collaborative filtering algorithm and sustainable PSCM are useful for the intelligent recommendation analysis of innovative entrepreneurial projects.