Intelligent Recommendation System Research Articles

Satisfaction with Behavior Trajectory Security Protection in Social Media Intelligent Recommendations

Abstract Intelligent recommender systems provide personalized recommendation for users based on their behavior trajectories. Intelligent recommendation is a double-edged sword with increasing impacts. This study investigates the influencing mechanism of social media users’ satisfaction with behavior trajectory security protection in the context of intelligent recommendation, with the aim of promoting healthy development of mobile social media. This study applied the grounded theory method to identify relations among concepts and categories in terms of three-level coding. During open coding, 271 initial concepts and 26 subcategories were elicited; during axial coding, 10 categories were elicited; and during selective coding, relations among categories were identified and a theoretical model was developed. The results indicate that satisfaction with behavior trajectory security protection is directly influenced by security protection information quality, security protection system quality, security protection service quality, net benefits, external support, privacy concerns, and privacy cognitive biases. Privacy literacy has direct impacts on privacy concerns and privacy cognitive biases. Meanwhile, herd behavior directly impacts privacy concerns. Implications for theory and practice are discussed.

Mitigating filter bubbles: Diverse and explainable recommender systems

In recent years, the surge in online content has necessitated the development of intelligent recommender systems capable of offering personalized suggestions to users. However, these systems often encapsulate users within a “filter bubble”, limiting their exposure to a narrow range of content. This study introduces a novel approach to address this issue by integrating a novel diversity module into a knowledge graph-based explainable recommender system. Utilizing the Movie Lens 1M dataset, this research pioneers in fostering a more nuanced and transparent user experience, thereby enhancing user trust and broadening the spectrum of recommendations. Looking ahead, we aim to further refine this system by incorporating an explicit feedback loop and leveraging Natural Language Processing (NLP) techniques to provide users with insightful explanations of recommendations, including a comprehensive analysis of filter bubbles. This initiative marks a significant stride towards creating a more inclusive and informed recommendation landscape, promising users not only a wider array of content but also a deeper understanding of the recommendation mechanisms at play.

An intelligent recommender system using machine learning association rules and rough set for disease prediction from incomplete symptom set

Digital devices are an integral component of the healthcare sector. With the advancement of modern technology with Artificial Intelligence (AI) and Machine Learning (ML), an automated diagnosis system with promising results is not a difficult task. This study aims to develop a recommender system (RS) for better diagnosis and improvement of patient care by hybridizing machine learning association rules (AR) and rough set theory (RST) to classify acute and life-threatening diseases. Initially data is preprocessed using binary, on-hot vector, and min–max scale to remove the noise. RST is used for feature selection to deal with incompleteness, inconsistency, and vagueness. We have designed an Associated Symptom Selection (ASS) algorithm to extract the mutually associated symptoms which need to be further matched in the existing database for prediction. ASS is especially helpful in detecting neurodevelopmental type diseases because the symptoms are usually not detectable by standard tests, and observations of behavioral expressions do general testing. The experiment is carried out using six popular ML classifiers such as AR, Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Linear Support Vector Machine (LSVM), and Naive Bayes (NB) on a publicly available datasets. Performance was compared among different classifiers regarding the accuracy, precision, recall, F1-score, and J-Score value. The experimental result shows that AR performs better on clinical data with an accuracy of 94.40%, precision of 90.73%, recall of 94.45%, F1-score of 92.55%, and J-score of 95.14% and on autism with 98.7% accuracy, 98% precision, 97.8% recall, 97.9% F1-score, and 97.12% J-score respectively.

Analysis of Business Intelligence Technology in the Big Data Era A Case Study of Alibaba Group

In the age of big data, business intelligence technology is pivotal in enhancing user experiences and driving innovation across industries. This paper focuses on Alibaba Group, a trailblazer in e-commerce, to examine the transformative role of business intelligence. This paper investigates Alibabas cutting-edge application of business intelligence technology, focusing on intelligent recommendation systems, personalized marketing strategies, and efficient supply chain management. The recommendation system harnesses data analysis to provide tailored product suggestions, boosting user satisfaction and sales. Data-driven marketing strategies enable Alibaba to create personalized promotions and coupons, enhancing user experiences and building loyalty. Intelligent supply chain management employs real-time monitoring, optimized transportation, and data-driven decisions to ensure timely deliveries and cost efficiency. A case study of Alibaba's "Singles Day Global Shopping Festival" illustrates how business intelligence technology creates a dynamic, data-powered shopping event. Every decision during this event is informed by real-time analysis and AI insights, enabling swift responses to evolving consumer needs. In summary, business intelligence, a driving force in the age of big data, is at the heart of Alibabas success. Alibaba has tapped the potential of business intelligence by enhancing the user experience and facilitating data-driven decision-making.

Understanding Senior Adults' Needs, Preferences, and Experiences of Commercial Exergames for Health: Usability Study.

Many senior adults are at risk of mental and physical disorders due to a lack of sufficient exercise. Therefore, adherent exercise should be urgently promoted to improve senior adults' muscle strength, preventing falls and conditions caused by physical and cognitive decline. However, off-the-shelf exercise games, so-called exergames, are mainly targeted at the younger generation or children, while senior adults are neglected, when this age group strongly needs exercise. Exergames could serve as a health intervention for promoting exercise. This study aimed to investigate senior adults' experience, perceptions, and acceptance of game technology to promote exercise in order to suggest game design guidelines. In this usability study, participants engaged in playing Nintendo Switch and Xbox Kinect games, after which semistructured interviews were conducted. Before the gameplay, the participants provided their background information, exercise habits, and use of technology products. Next, all participants completed a workshop including 3 activities (brief instructions on how to play the games: 20 minutes; playing the selected exergames: 80 minutes; semistructured interviews: 20 minutes) for 2 hours a day for 3 days each. The participants played the latest Nintendo Switch games (eg, Just Dance, Boxing, Ring Fit Adventure) and Xbox Kinect games (eg, Kinect Adventures!, Mini Games). Just Dance, Zumba, and Boxing were played in activity 1; Ring Fit Adventure and Mini Games in activity 2; and Kinect Adventures! in activity 3. Reflexive thematic analysis was applied to identify the relative themes generated from the interviews. In total, 22 participants (mean age 70.4, SD 6.1 years) were enrolled in the workshop in May 2021. The results of the generated themes included incomprehension of game instructions, psychological perception of game technology, and game art preferences. The subthemes generated from game art preferences included favorite game genres, characters, and scenes. There is a significant need for customized game tutorials considering senior adults' cognitive and physical aging. Furthermore, the adventure game genre is preferable to other games. Humanlike game characters are preferable, especially those with a fit and healthy body shape. Nature scenes are more enjoyable than indoor stages or rooms. Furthermore, the game intensity design and playing time should be carefully planned to meet the World Health Organization's criteria for physical activity in older adults. Intelligent recommendation systems might be helpful to support older adults with various health conditions. The guidelines suggested in this study might be beneficial for game design, exercise training, and game technology adoption of exergames for older adults to improve health.

DLSF: Deep learning and semantic fusion based recommendation system

An intelligent recommendation system facilitates users by recommending items based on the user’s preferences. Most of the existing recommender systems employ collaborative filtering which consider homogenous data only and does not exploit different knowledge sources. Such approaches encounter many challenges such as Cold Start problem, Sparsity, and Scalability. To overcome these setbacks, a novel deep learning & semantic fusion-based recommendation system named DLSF is proposed in this paper which collaborates multiple knowledge sources by employing three modules to generate top-N recommendations. The first module precludes sparsity by using latent representations and deep learning for understanding user-item interactions. The second module exploits semantic information provided to similar items by the users for dealing with cold start problem. And the third module works for new users and items which don’t have any prior information. A comprehensive experimental evaluation is conducted over benchmark datasets. The proposed DLSF showed on average 16.02% and 16.71% more efficient over the baselines recommendation systems taken into account in terms of MAE and RMSE respectively. DLSF has found to have significant efficiency in terms of Precision, Recall and overall accuracy.

Intelligent recommendation system for College English courses based on graph convolutional networks

With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes a graph convolutional neural network model based on College English course texts, students’ major, English foundation and network structure characteristics. First, by analyzing the relevant data of College English courses and combining with graph neural network, an English course recommendation algorithm model based on the College English learning strategy of proximity comparison is proposed. Then, the College English texts are taken as feature input, and multi-layer graph convolutional neural network is used to process the above graph neural network structure. Attention mechanism is introduced to enhance the representation of graph features in College English skills. Finally, multi-layer attention model is used to process the courses that users have learned, and intelligent course recommendation is made by combining the multi-layer attention modeling of College English skills. The experimental data show that the proposed method achieves the best performance compared with the commonly used College English course recommendation method.

An intelligent recommendation system for personalised parametric garment patterns by integrating designer’s knowledge and 3D body measurements

Garment pattern-making is one of the most important parts of the apparel industry. However, traditional pattern-making is an experience-based work, very time-consuming and ignores the body shape difference. This paper proposes a parametric design method for garment pattern based on body dimensions acquired from a body scanner and body features (body feature points and three segmented body part shape classification) identified by designers according to their professional knowledge. By using this method, we construct a men’s shirt pattern recommendation system oriented to personalised fit. The system consists of two databases and three models. The two databases include a relational database (Database I) and a personalised basic pattern (PBP) database (Database II). The Database I is based on manual and three-dimensional (3D) measurements of human bodies by using designer’s knowledge. And Database I is a relational database, which is organised in terms of the relational model of the body part shape and its key body feature dimensions. After a deep analysis of measured data, the irrelevant measured dimensions to human body shape have been excluded by designers and extract representative human body feature dimensions. In addition, the relations between body shapes and previously identified body feature dimensions have been modelled. From the above relational model, we label key feature point positions on the corresponding 3D body model obtained from 3D body scanning and correct the whole 3D human upper body model into the semantically interpretable one. The 3D personalised basic pattern is drawn on the corrected model based on these key feature points. By using three-dimensional to two-dimensional (3D-to-2D) flattening technology, a 2D flatten graph of the 3D personalised basic pattern of the interpretable model is obtained and slightly adjusted to the form suitable for industrial production, i.e., PBP and the PBP database (Database II) is built. In addition, the three models include a basic pattern parametric model (Model I) (characterizing the relations between the basic pattern and its key influencing human dimensions (chest girth and back length)), a regression model (Model II) which enables to infer from basic pattern to PBP for three body parts based on the one-to-one correspondence of key points between the PBPs and the basic patterns and a personalised shirt pattern parametric model (Model III) (characterizing the structural relations between the personalised shirt pattern (PBPshirt) and PBP). The initial input items of the recommendation system are the body dimension constraint parameters, including chest girth, back length and the body feature dimensions used to determine each body part shape as well as three shirt style constraint parameters (slim, regular and loose). By using Model I, the corresponding basic pattern can be generated through the user’s chest girth and back length. Body feature dimensions determine the three body parts’ shapes. Then, Model II is used to generate the PBP for the corresponding body parts shape. Based on the shirt style chosen by the user, Mode III is used to generate the PBPshirt from the PBP. The output of the recommendation system is a fit-oriented PBPshirt. Moreover, if the PBPshirt is unsatisfactory after a virtual try-on, four adjustable parameters (front side-seam dart, back side-seam dart, waist dart and garment bodice length) are designed to adjust the PBPshirt generated by the proposed recommendation system.

Intelligent Recommendation System for Personalized Learning Resources for College Students Based on Image Processing

Intelligent Recommendation System for Personalized Learning Resources for College Students Based on Image Processing

A novel deep learning and Internet of Things (IoT) enabled precision agricultural framework for crop yield production

<p>Precision agriculture is a growing concept that frequently refers to enhancing farms via the use of up-to-date knowledge and cutting-edge technology, which in turn aids farmers by automating and improving them to increase rural profitability. This paper suggests the novel framework Deep-Plant-IoT which amalgamates the Internet of Things (IoT) and Deep learning framework for an effective prediction of crop yields which act as intelligent recommendation systems that can significantly improve the production. The framework incorporates IoT sensors and devices to collect and store the soil parameters in the cloud. Then these data are downloaded offline and the Harris Hawk Optimized Long Short Term Memory network is deployed to effectively predict crop yields that can aid in better production. Nearly 15902 data were collected for two months and Extensive testing was undertaken to employ these data to evaluate and analyze the proposed framework. Moreover, the prediction algorithm proposed in the framework is evaluated in comparison to other cutting-edge learning models. The suggested algorithm has demonstrated greater performance such that 98% accuracy, 97.23% precision, 97.0% recall, and 97.2% F1-score respectively.</p>

Retracted: Personalized Education Based on Hybrid Intelligent Recommendation System

Retracted: Personalized Education Based on Hybrid Intelligent Recommendation System

Disentangled self-attention neural network based on information sharing for click-through rate prediction.

With the exponential growth of network resources, recommendation systems have become successful at combating information overload. In intelligent recommendation systems, the prediction of click-through rates (CTR) plays a crucial role. Most CTR models employ a parallel network architecture to successfully capture explicit and implicit feature interactions. However, the existing models ignore two aspects. One limitation observed in most models is that they focus only on the interaction of paired term features, with no emphasis on modeling unary terms. The second issue is that most models input characteristics indiscriminately into parallel networks, resulting in network input oversharing. We propose a disentangled self-attention neural network based on information sharing (DSAN) for CTR prediction to simulate complex feature interactions. Firstly, an embedding layer transforms high-dimensional sparse features into low-dimensional dense matrices. Then, the disentangled multi-head self-attention learns the relationship between different features and is fed into a parallel network architecture. Finally, we set up a shared interaction layer to solve the problem of insufficient information sharing in parallel networks. Results from experiments conducted on two real-world datasets demonstrate that our proposed method surpasses existing methods in predictive accuracy.

A novel processor for dynamic evolution of constrained SAT problems: The dynamic evolution variant of the discrete Hopfield neural network satisfiability model

The Boolean satisfiability problem, a renowned NP-complete challenge in computer science, has recently garnered interest in the Discrete Hopfield Neural Network - Satisfiability model. This model adeptly integrates logical rules into Hopfield networks, excelling in locating global minima for traditional SAT problems. However, it faces efficiency challenges when dealing with SAT problems characterized by dynamic evolution constraints due to its static network architecture. During dynamic evolution iterations, there is a significant exponential increase in the computational costs due to redundant and repetitive computations.In order to address this challenge, this paper introduces a dynamic evolution variant of the Discrete Hopfield Neural Network - Satisfiability model. In extensive simulation experiments, we progressively augmented the number of constraint clauses from 1 to 1500, seeking global minima for CNF problems. The proposed model exhibited congruent performance with the traditional model, achieving a Global Minimum Ratio of 1 and a Hamming distance of 0. Crucially, the proposed model minimized CPU utilization and neared zero error metrics, while the traditional model experienced exponential CPU and error metric escalation. These outcomes affirm the proposed model's robust global search capabilities and high precision, aligning with the traditional model. Furthermore, owing to this model's incorporation of not only temporal constraint increment operator but also innovative real-time learning techniques and clever integration methods, along with the establishment of a novel real-time decision mechanism, the proposed model effectively addresses the issues of redundancy and repeated calculations inherent in traditional models. This results in a stable and significantly improved computational speed. Additionally, this model's dynamic evolution network architecture is specifically designed to accommodate arbitrary and efficient extensions of dynamic constraints, while also featuring a built-in preprocessing function to filter out duplicate variables. In the future, as a dynamic evolution processor, this model exhibits immense potential for solving large-scale SAT problems with dynamic evolution constraints and holds broad application prospects in emerging fields such as automated reasoning, intelligent recommendation systems, and machine learning.

Research on Intelligent Recommendation of Computer Course Items Combining Augmented Learning Algorithms and Data Mining Techniques

Abstract This paper combines augmented learning algorithms and data mining techniques for an intelligent recommendation system for computer course projects to optimize the allocation of learning resources and improve learning efficiency and effectiveness. The research methodology includes an improved association rule mining algorithm and an augmented learning model applying deep Q-network and adaptive reward function. The research results show that the system can effectively mine knowledge point associations and achieve personalized recommendation. In particular, the accuracy of knowledge point associations and the customized level of recommendation are improved by multi-support strategy improvement and knowledge point association correction. Experiments show that on the edX dataset, the recommendation recall reaches 0.347 at k=10 and 0.433 at k=20, significantly better than the traditional recommendation algorithm. This intelligent recommendation system can effectively improve the learning effect of computer course projects, and it has a positive impact on both learning attitude and independent learning ability, especially helping the backward students significantly and reducing the learning differences between classes.

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

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

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.

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.