Deep learning-driven tourist behavior prediction and intelligent recommendation system for smart tourism platforms

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.

Due to the low cognitive ability of children, the intelligent recommendation system should assist children in learning with the assistance of parents. Therefore, the intelligent recommendation system needs to evaluate the ability of users (children learners and their parents), and assign learning content with suitable difficulty Match with the user’s ability, assist the user to learn efficiently, and gradually cultivate the user’s learning interest and habits, and can be a useful supplement to the user’s regular learning. This paper uses JAVA EE language to develop an intelligent recommendation system for children’s learning platform based on Hadoop big data framework and machine learning algorithms. Learning platform with intelligent recommendation function. By analyzing the cognitive ability of children, and using the collaborative recommendation algorithm to recommend learning resources suitable for this age stage for children, in order to improve learning efficiency and cultivate children’s learning interest from an early age, at the same time, the resource library in the learning platform has more Practical value.

The rapid growth of big data has created new opportunities for developing intelligent systems capable of supporting effective decision-making processes. In this context, recommendation systems play a crucial role by providing personalized and data-driven suggestions to users. However, traditional recommendation approaches often face limitations in scalability, accuracy, and the ability to process large-scale heterogeneous data. This paper focuses on the design of an intelligent recommendation system based on big data analytics. The study analyzes modern big data technologies and machine learning methods used for collecting, processing, and analyzing large volumes of structured and unstructured data. A system architecture is proposed that integrates data preprocessing techniques, big data platforms, and intelligent algorithms to improve recommendation quality. Experimental results demonstrate that the proposed approach enhances recommendation accuracy and system performance compared to conventional methods. The findings highlight the effectiveness of big data–driven analytics in developing scalable and intelligent recommendation systems. The results of this study can be applied in various domains such as e-commerce, education, and decision support systems, and provide a foundation for future research in intelligent data-driven system design.

The introduction of computerized formative assessments in the classroom has opened a new area of effective progress monitoring with more accessible test administrations. With computerized formative assessments, all students could be tested at the same time and with the same number of test administrations within a school year. Alternatively, the decision for the number and frequency of such tests could be made by teachers based on their observations and personal judgments about students. However, this often results in rigid test scheduling that fails to take into account the pace at which students acquire knowledge. To administer computerized formative assessments efficiently, teachers should be provided with systematic guidance regarding effective test scheduling based on each student's level of progress. In this study, we introduce an intelligent recommendation system that can gauge the optimal number and timing of testing for each student. We discuss how to build an intelligent recommendation system using a reinforcement learning approach. Then, we present a case study with a large sample of students' test results in a computerized formative assessment. We show that the intelligent recommendation system can significantly reduce the number of testing for the students by eliminating unnecessary test administrations where students do not show significant progress (i.e., growth). Also, the proposed recommendation system is capable of identifying the optimal test time for students to demonstrate adequate progress from one test administration to another. Implications for future research on personalized assessment scheduling are discussed.

Design of Intelligent Advertising Recognition System Based on Intelligent Recommendation

With the fast adoption of artificial intelligence in the art and cultural industry, the production, curation, distribution, and management of creative works have been radically transformed. Intelligent systems that allow artists, curators, institutions, platforms, and intelligent systems to work together in continuous interaction are now known as AI-driven art ecosystems. The paper explores management innovation as it manifests in AI-based art systems, the changes in managerial practices, forms of governance and decision making, in reaction to advanced computational creativity and data-driven work. The paper conceptualizes AI-based art systems as multi-layered systems that include creative production, curatorial intelligence and digital distribution systems such as online galleries and non-fungible token-based markets. It emphasizes the ways in which management innovation is developed in the form of a workflow redesign that combines automation and human-AI partnership to allow efficiency without sacrificing artistic intent and cultural sensitivity. Additionally, the paper focuses on the governance innovations that respond to the issues of transparency, accountability, ethical compliance, and authorship attribution in creative settings with algorithms mediating them. The resource orchestration is considered a key managerial competency with a focus on the strategic alignment of data resources, innovative talent, and computing resources. The study further examines the AI-enhanced decision-making in the context of art institutions and how the predictive analytics and the intelligent recommendation systems can be used in audience engagement prediction, curatorial planning, and portfolio management. Based on the selected case studies of AI-integrated museums, hybrid creative studios, and global AI-art hubs, the paper finds the best practices and benchmarking perspectives.

An improvisation version of traditional wireless networks provide mobile ad hoc network (MANET), which is highly suitable for emergency needs. But, at the same time, due to its infrastructure less and resources limitation create many challenges in its performance. Mainly due to its dynamic behaviour it prones to high security concern, as the complete communication cycle depends on the independent nodes which voluntary join and leave. Several security studies reveal that trust management systems can be effective and attained low overhead. But, most of these studies used an automated decision algorithms based on some predefined trust scheme. This kind of scheme unable to distinguish the node intentional and unintentional errors at runtime, which impact the selected paths for the communication. So, it is very important to identify node behaviour and also provide node recovery scheme. In this paper, we propose a novel trust management system based on node behaviour predication algorithm to preserve high network stability and security for the reliable data delivery. The algorithm will predict and identify the node behaviour and distinguish between unintentional temporary errors and intentional malicious behaviour and compute a node overall trust. Experimental results show that, node behavior prediction and trust management system improvise the throughput ratio and network stability.

The alarming loss of forested areas containing red pine (Pinus resinosa Ait.) and eastern white pine (Pinus strobus L.) in eastern Canada is a situation that must be addressed promptly by changing management approaches. Since the ecological role of fire in the regeneration and perpetuation of these pines is well known, it makes sense to use fire to maintain pine ecosystems through forest management that uses an understory prescribed burn program. Fears of fire escape and a poor knowledge of applying fire correctly to these ecosystems are the result of poor training in the use of prescribed fire, which normally concentrates solely on post-harvest slash sites; this has prevented extensive use of understory prescribed burning in the past. However, research in Canada, principally using the Canadian Forest Fire Behavior Prediction (FBP) System coupled with the Canadian Forest Fire Weather Index (FWI) System, allows forest managers to develop burning prescriptions that are safe and economical while meeting objectives for seedbed preparation, natural seeding and control of competing vegetation. Key words: crown scorching, eastern white pine, fire, natural regeneration, red pine, seedbed preparation, understory prescribed burning, vegetation control.

Among thousands of alternatives, most of the time online customers cannot easily decide on which product to purchase or service to utilize. In order to assist online customers in their decision-making process, business owners have started to make their online platforms more intelligent by enhancing their platforms with intelligent recommender systems. Recommender systems, also known as recommender agents or intelligent agents, are intelligent software that provide easily accessible, personalized, highly relevant, and high-quality recommendations to customers in various online platforms. This chapter discusses different types of recommender systems and provides use case examples of recommender systems in various e-commerce platforms. This chapter shows how recommender systems make life easier for online customers in the constantly developing and growing internet environment. This chapter also discusses the challenges posed by recommender systems to online customers.

In the ever-changing realm of E-Commerce, it is essential for online businesses to comprehend and adjust to shifting consumer behaviour in order to achieve long-term success. In which, Intelligent Recommendation System (IRS) has gained familiarity by suggesting personalized information based on user preference and behaviours. Hence, the review paper primarily aims to analyse significance of the intelligent recommendation system to transform ecommerce field, specifically enrich the user personalisation and satisfaction, and enhance revenue in business. Accordingly, the proposed survey is discussed the traditional system and AI-powered personalization system in ecommerce. AI-powered recommendation system utilize sophisticated algorithms to analyse extensive data, allowing for the provision of highly customized and relevant content, product recommendation, and user satisfaction. Besides, it examines future trends in AI integration within e-commerce, particularly advancements in Natural Language Processing (NLP) and visual search technologies, which are poised to further enrich ecommerce. The paper concludes with a look toward future directions for the integration of AI technologies in e-commerce, anticipating advancements in NLP and visual search capabilities, which promise to further enhance the online shopping experience. Overall, the findings of the article underscores the transformative impact of IRS on the e-commerce sector, advocating for their continued development in response to evolving market demands.

This research proposes the method to develop the recommendation system of automobile parts using task ontology technology. The proposed intelligent recommendation system is designed to learn the assembly process of automobile parts and the automobile parts are composed by ontology method for the recommendation of the parts. Using hierarchical taxonomy based on is-a relationship, the relationship between each part that makes up automotive engine was set. Each part has each different weighted value according to the knowledge of automobile experts. The weighted value is created by the number of selection that the users of the automobile recommendation system select while using the system and the final value calculated by the multiplication of the weighted value, which is recorded within the system. As a result, the users can easily identify which factor in which part is important by the output in the order of the priority. The intelligent recommendation system for automobile parts is a system to inform of the assembly, the usage and the importance of automobile parts without any specialized knowledge by expressing the parts that are closely related with the applicable parts when selecting any part on the basis of the generated data for the automobile parts that are difficult to access by users.

The research aims to solve the challenge of filtering out the content that users are really interested in from massive data. In this paper, an intelligent recommendation system named multi-source fusion deep collaborative recommendation algorithm (MSDC-Rec) is proposed, which combines collaborative filtering, content filtering and deep learning (DL) technology to improve the accuracy and diversity of the recommendation system. MSDC-Rec algorithm integrates user behavior data, item attribute data and social network information, and uses deep neural network for advanced feature extraction and pattern learning, thus accurately portraying user portraits and realizing personalized and accurate recommendation services. The experimental results show that MSDC-Rec is superior to the traditional algorithm in accuracy, recall, F1 score and personalized index, and has low root mean square error (RMSE) and acceptable real-time. This study not only solves the problems of data sparsity and cold start, but also significantly improves the accuracy and personalization of recommendation, which provides a new idea for the development of intelligent recommendation system.

Background/Objectives: This paper aims to develop a novel intelligent recommender system suitable for high dimensional data where multiple factor variables influence on multiple response variables. Methods/Statistical Analysis: This paper suggests that the structure of the cause-and-effect relations among the variables can be represented in a simple form called structured association network model (SANM). Based on the SANM and conventional data mining techniques such as association rule mining and naive Bayesian classifier, the proposed recommender system computes three novel recommendation scores for each response variables, and the variables with high scores can be selected for recommendation. Findings: For illustration, the proposed recommender system has been applied to a mass health examination result data set. Owing to its simple structure, a SANM for a given data set can be easily obtained by simply identifying the factor and the response variables, and the experiment results revealed that the proposed system can identify the recommendable items for the individuals more effectively than the traditional classification techniques such as naive Bayesian classifier. Consequently, we can conclude that the proposed recommender system can deal with high dimensional transaction data set in more effective manner than the traditional approaches where the underlying semantic relationships among the variables are not considered. Applications/Improvements: The proposed recommender system is useful for evaluation of the potential risks of specific diseases. Moreover, the recommendation scores can also be used as a tool for feature construction.

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.

The article considers the essence and content of the process of making managerial decisions in the activities of higher education institutions. It is determined that a quality management decision in the context of higher education institutions is the result of analysis, forecasting, economic justification and choice of alternatives from a variety of options aimed at achieving sustainable development of higher education institutions. The essence of the intelligent recommendation system of management decision support is described, the use of which allows managers to combine their own subjective advantages with machine analysis of the situation in making recommendations in the management decision-making process aimed at achieving certain goals of higher education. The advantages of an intelligent recommendation system in comparison with traditional automated management decision support systems are determined. The main functions of the intelligent recommendation system to support management decisions are considered, including: situation analysis; generation of possible management decisions to solve the tasks; evaluation of generated scenarios and selection of the best; ensuring constant exchange of information on the status of the decision and coordination of group decisions; modeling of decisions; computer analysis of possible consequences of management decisions; collection of data on the results of implementation of management decisions and evaluation of their results. The main ways to improve the quality of management decisions in the activities of higher education institutions to ensure their sustainable development are identified. The expediency of using an intelligent recommendation system of decision support to improve the quality of management decisions in the activities of higher education institutions in order to achieve sustainable development is substantiated. It is proposed to combine the knowledge and experience of managers with automated support of this process to improve the quality of management decisions in the activities of higher education institutions and to achieve sustainable development, which will increase the validity of management decisions through the use of intelligent recommendation system to support management decisions.