Smart Tourism using Attractive and Safe Travel Recommendation Technology

The tourism sector was among the most affected sector during the COVID-19 pandemic and has lost up to USD 5.87 billion potential revenue. Since many countries closed the borders, including Indonesia, by applying travel restrictions and thus tourists postponed their visits. Whereas vaccine distribution has shown good progress as the vaccination percentage in Jakarta and Bali has shown promising results since the majority of its population has been vaccinated, and it helps many industries, including tourism, recover. However, the pandemic might change tourist behavior. In addition, information about tourist destinations is spread poorly in various sources, and it psychologically affects tourists' decision to visit. Many works have been published to address this issue with the recommendation system. However, it does not provide geopolitical variables such as PPKM in Indonesia to ensure safeness for the tourist. Therefore, this research aims to enhance innovations in the tourism industry by considering the geopolitics factor into the system using Multiple Linear Regression. The result of this research demonstrates the effectiveness of geopolitics added variable on three different cities Jakarta, Java, and Bali. It can be implemented in a wide area in Indonesia. For further research, the proposed model can be used in a wide area in Indonesia and developed for a more comprehensive recommendation system. © 2022 IEEE.

OPEES: Online Proctored Entrance Examination System with Degree Program Recommender for Colleges and Universities

The college entrance examination is vital for program admission. Typically, entrance examinations are conducted onsite using paper and pens. When the COVID-19 pandemic hit, the entrance examination was lifted and physical gatherings were prohibited. Since many schools cannot offer an online admissions exam, they rely on grades and interviews to admit and qualify students for degree programs. However, academic standards differ between schools, and grades may not be enough to assess students' capacity. Thus, this study aims to develop an Online Proctored Entrance Examination System (OPEES) with Degree Program Recommender for colleges and universities to help institutions administer onsite or online entrance tests and generate course suggestions using a rulebased algorithm. The study employed the scrum methodology in software development. OPEES allows applicants to submit applications online, and institutions can manage user accounts, tailor exams and degree programs' criteria, manage exam dates, and assign proctors. Online proctoring using Jitsi, an opensource multiplatform voice, video, and instant messaging tool with end-to-end encryption, ensures exam integrity. The system's features were evaluated by 102 respondents, comprised of end-users (students and school personnel) and IT professionals, using the FURPS (Functionality, Usability, Reliability, Performance, and Supportability) software quality model. In the software evaluation, the overall system proved to be functional as perceived by the respondents, as manifested by the mean rating of 4.61. In conclusion, the system's architecture was deemed feasible and offers a better way to streamline admission examinations and determine a student's applicable degree program by enabling institutions to customize their exams and degree program requirements. It will be beneficial to look into recommendation system algorithms and historical enrollment data to improve the system's use case. © 2022 IEEE.

Recommender System for Government Service in Smart Cities

significant recommender systems (RS) development has occurred along with the Internet of Things (IoT) development in recent years. Recommender systems have been widely spread across diverse fields, including environmental preservation, e-commerce, healthcare, social and governance systems. There has been a growing focus on e-government as part of smart city initiatives in today's world of connected devices and infrastructure, especially after the COVID-19 pandemic. With the use of information and communication technologies (ICTs), the government can enhance the delivery of public services, increase transparency, accountability, and credibility, as well as engage citizens in the decision-making process. To facilitate 'smart' governance, one of a smart city initiative's objectives is integrating e-government into the city's governance framework. The lack of personalized services for particular stakeholders is one of the most significant limitations of e-governance. There are a number of open challenges coupled with interesting opportunities, making this a very promising and exciting area for research to shape recommendation systems for urban environments. Considering the overwhelming amount of information, services, and tasks available through smart government applications, it is a greater chance of providing personalized recommendations for different stakeholders and tasks within multi-faceted and multi-dimension. There is still a lot of research to be done on recommendation systems in the context of smart cities or smart government. This paper survey the existing studies on recommendation systems for smart governance. The study aims to address smart city challenges to considered when designing and implementing recommendations for e-governance and the target stakeholder's interests. © 2022 IEEE.

A drug recommender system Based on Collaborative Filtering for Covid-19 patients

The epidemic caused by a new mutation of the coronavirus family called Covid-19 has created a global crisis involving all the world's countries. This disease has become a severe danger to everyone due to its unknown nature, high spread, and inability to detect the infected. In this regard, one of the important issues facing patients with Covid-19 is the prescription of Drugs according to the severity of the disease and considering the records of underlying diseases in people. In recent years, recommender systems have been developed significantly along with the advancement in information technology and artificial intelligence, which is one of its applications in various fields of medical sciences. Among them, we can refer to recommending systems for the prevention, control, and treatment of diseases. In this research, using the collaborative filtering approach as one of the types of recommender systems as well as the K-means clustering algorithm, a Drug recommendation system for patients with Covid-19 in the treatment stage of the disease is presented. The results of this research show that this recommender system has an acceptable performance based on the evaluation criteria of precision, recall, and F1-score compared to the opinions of experts in this field. © 2023 IEEE.

Learning Peer Recommendation Based on Weighted Heterogeneous Information Networks on Online Learning Platforms

With the development of online education, there is an urgent need to solve the problem of the low completion rate of online learning courses. Although learning peer recommendation can effectively address this problem, prior studies of learning peer-recommendation methods extract only a portion of the interaction information and fail to take into account the heterogeneity of the various types of objects (e.g., students, teachers, videos, exercises, and knowledge points). To better motivate students to complete online learning courses, we propose a novel method to recommend learning peers based on a weighted heterogeneous information network. First, we integrate the above different objects, various relationships between objects, and the attribute values to links in a weighted heterogeneous information network. Second, we propose a method for automatically generating all meaningful weighted meta-paths to extract and identify meaningful meta-paths. Finally, we use the Bayesian Personalized Ranking (BPR) optimization framework to discover the personalized weights of target students on different meaningful weighted meta-paths. We conducted experiments using three real datasets, and the experimental results demonstrate the effectiveness and interpretability of the proposed method.

The Voice of Major E-Tourism Players: An Expedia Group Perspective

This chapter outlines the approach of Expedia Group, the world's travel platform, and the role of technology in revolutionizing travel search, discovery, and booking. It covers innovations developed by online travel agencies (OTAs) and the unique challenges and opportunities provided by the breadth and depth of the data that global OTAs leverage to power travelers' online experiences. The focus is on accommodation, the largest revenue-generating, and most complex tourism segment. The chapter explores specific use cases where data are brought together with leading and innovative machine learning methodologies to improve traveler and supplier experiences. They include recommender systems, machine learning models that help Expedia Group manage the text and image content for over a million properties and revenue management systems for accommodation providers. The chapter concludes with a comment on the Expedia Group COVID- 19 response. © Springer Nature Switzerland AG 2022.

Execution of Market Basket Analysis and Recommendation Systems in Physical Retail Stores to Advance Sales Revenues

In the early months of 2020, pandemic covid-19 hit many parts of the world. Especially developing countries like India observed a negative growth rate in few quarters of last financial year. Retailing is one of the key sectors that contribute to Indian GDP with a share of nearly 10 percent. Hence there is a need for the retail sector to bounce back which is possible with the efficient use of new digital technologies. Market basket analysis is used here to extract the association rules which can be directly used for formulating discount and combo offers. Along with that, these rules can be used to decide the product positioning in the retail store. Items which are bought together can be placed next to each other to increase sales. Recommendation systems are most commonly used in ecommerce websites like Amazon, Flipkart, etc, and streaming platforms like Netflix to recommend the items that are to be purchased by users. Although recommendation engines are implemented in multiple web and mobile applications, these are not in the implementation stage in offline retail stores due to many implications associated with them like infrastructure, cost, etc. In this project, we have used market basket analysis and recommendation systems to propose a model to implement in retail stores to increase sales revenues and enhance customer experience. © 2022 IEEE.

Sistema de recomendación integrado para el autocontrol emocional en adultos mayores con cuadro postcovid-19 en Panamá

: The effects of the pandemic can translate into a variety of physical and emotional reactions that are affecting the population, particularly the elderly Panamanian population, who have not been able to overcome the mainly emerging challenges of an infectious disease with health implications. physical and has also profoundly affected their well-being and mental health. To allow the Panamanian elderly population to improve emotional self-control and mental relaxation, we propose a software architecture for the development of a recommendation system integrating: artificial intelligence (AI), internet of things (IoT) and mobile applications. Keywords: Covid-19, AI, IoT, Mobile apps, Machine learning. 1.Introducción La Covid-19 es, sin lugar a duda, la mayor catástrofe del siglo XXI, probablemente la crisis global más significativa después de la segunda guerra mundial. En este artículo, proponemos el diseño de una arquitectura altamente integral y flexible basada en diferentes elementos de TIC que permitirá extraer datos de un sensor, analizarlos y realizar recomendaciones a pacientes panameños adultos mayores con afecciones psicológicas o reacciones emocionales posteriores al contagio de la Covid-19 (post-covid-19), basado en la utilización de componentes como IA, IoT y aplicaciones móviles para lograr el autocontrol emocional y relajación mental.

Research and Application of Online Course Recommendation System Based on TF-IDF Algorithm

In recent years, with the rapid development of Internet technology, a large number of online learning resources have emerged. Especially affected by the COVID-19 epidemic, online learning has become a very effective learning means. However, a large number of learning platforms and massive online teaching resources have the following three problems: 1) The quality of these courses is uneven and the evaluation standards are different;2) There are so many similar courses that it is difficult for learners to distinguish them;3) These classes are lack of unity and integration, and it is hard to recommend any hierarchical, coherent and systematic course resources to learners. Therefore, a recommendation model based on TF-IDF algorithm is designed to extract personalized-featured courses, use the nearest neighbor similarity to cluster the similarity of similar courses, and conduct the featured portrait of learners to realize online courses recommendation. Combined with the model design, this paper presents a tag-based online course resource recommendation system, which can fully explore learners' explicit and implicit preferences according to course tags, and recommend satisfactory MOOC resources for them with good application value. © 2022 IEEE.

Consumer Behavior Prediction During Covid-19 Pandemic Conditions Using Sentiment Analytics

Covid-19 pandemic created a global shift in the way how consumers purchase. Restrictions to movements of individuals and commodities created a big challenge on day today life. Due to isolation, social media usage has increased substantially, and these platforms created significant impact carrying news and sentiments instantaneously. These sentiments impacted the purchase behavior of consumers and online retailers witnessed variations in their sales. Retailers used various customer behavior prediction models such as Recommendation systems to influence consumers and increasing their sales. Due to Covid-19 pandemic, these models may not perform the same way due to changes in consumer behavior. By integrating consumer sentiments from online social media platform as another feature in the prediction machine learning models such as recommendation systems, retailers can understand consumer behavior better and create Recommendations appropriately. This provides the consumers with appropriate choice of products in essential and non-essential categories based on pandemic condition restrictions. This also helps retailers to plan their operations and inventory appropriately. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Combined Application of Various Techniques for Personalized Job Recommendation

The Personalized Job Recommender System is a subset of the custom recommendation system that provides a solution to the problem of information overload and is widely applied in numerous domains to solve a plethora of problems, such as unemployment and employment churn that we have seen emerging at higher rates in the COVID era. Furthermore, different jobs require divergent skill sets from their candidates to get hired. In this paper, we analyze the similarity techniques for Job Recommendation Systems based on the research done in the field of Job Recommendations. In our implementation, we have used three similarity measures: Tanimoto, Cosine (Orchini), and City Block similarity metrics. These techniques have been tested on a new Job Recommendation Systems Dataset taken from Kaggle. We have also analyzed the performance of similar techniques involving other distance measures, such as Euclidean distance. The performance of these similarity score-based techniques for generating the highest score-based recommendations is assessed using different evaluation metrics such as Accuracy, Precision, Recall, and F1-score respectively. © 2023 IEEE.

Yoga Recommendation System for the Mental Well-Being of Students using Machine Learning

Many expectations placed on students by society have made stress a part of their academic lives. Youth are susceptible to the issues brought on by academic stress since they are going through a phase of transitions in both aspects i.e personal and social. Academic stress has been shown to lower academic achievement and lower motivation toward academics. Therefore, it becomes crucial to develop appropriate and effective intervention options. In recent times, due to COVID, the utilization of online health blogs and sites recommending health, exercise, and yoga has been significantly increased. The blog will provide solution to a problem and then provide precautions to common people but they lack the dynamics to suggest yoga that can be done any person or a personalized yoga by considering their health condition and not a static article. This research work intends to develop an AI model to predict the possible practices a student can do to alleviate their problem by considering their BPM, blood pressure (both systole and diastole), sleep time and some questions related to stress. The proposed stress prediction model has achieved an accuracy of 94.4% and the yoga pose recommendation system has achieved an accuracy of 97.3%. © 2022 IEEE.

Framework Design of Intelligent Career Recommendation System for Undergraduates

The development of artificial intelligence technology has proudly enhanced the quality of life and education of students. The outbreak of COVID-19 in early 2020 dealt a huge blow to the world economy and workplace environment, therefore planning a career path before graduation is a primary and core task for undergraduate students to succeed in this era. This paper introduces the framework design of an intelligent career recommendation system, which is based on the analysis of the required career ability and students' individual ability to achieve accurate career recommendations. © 2022 IEEE.

International Workshop on Learning with Knowledge Graphs: Construction, Embedding, and Reasoning

A knowledge graph (KG) consists of numerous triples, in which each triple, i.e., (head entity, relation, tail entity), denotes a real-world assertion. Many large-scale KGs have been developed, e.g., general-purpose KGs Freebase and YAGO. Also, lots of domain-specific KGs are emerging, e.g., COVID-19 KGs, biomedical KGs, and agricultural KGs. By embedding KGs into low-dimensional vectors, i.e., representations of entities and relations, we could integrate KGs into machine learning models and enhance the performance of many prediction tasks, including search, recommendations, and question answering. During the construction, refinement, embedding, and application of KGs, a variety of KG learning algorithms have been developed to handle challenges in various real-world scenarios. Moreover, graph neural networks have also brought new opportunities to KG learning. This workshop aims to engage with active researchers from KG communities, recommendation communities, natural language processing communities, and other communities, and deliver state-of-the-art research insights into the core challenges in KG learning. © 2023 Owner/Author.

Graggle: A Graph-based Approach to Document Clustering

Document recommendation systems have traditionally relied upon high-dimensional vector representations that scale poorly in corpora with diverse vocabularies. Existing graph-based approaches focus on the metadata of documents and, unfortunately, ignore the content of the papers. In this work, we have designed and implemented a new system we call Graggle, which builds a graph to model a corpus. Nodes are papers, and edges represent significant words shared between them. We then leverage modern graph learning techniques to turn this graph into a highly efficient tool for dimensionality reduction. Documents are represented as low-dimensional vector embeddings generated with a graph autoencoder. Our experiments show that this approach outperforms traditional document vector-based and text autoencoding approaches on labeled data. Additionally, we have applied this technique to a repository of unlabeled research documents about the novel coronavirus to demonstrate its effectiveness as a real-world tool. © 2022 IEEE.

Execution of market basket analysis and recommendation systems in physical retail stores to advance sales revenues

Pandemic COVID-19 struck numerous regions of the planet in the first few months of 2020. India and other emerging nations in particular saw negative growth over a few quarters of the previous fiscal year. With a contribution of over 10%, retailing is one of the major industries that contribute to India's GDP. As a result, the retail industry must recover, which may be done with the effective application of new digital technology. Here, association rules that may be utilised to create discounts and package deals are extracted using market basket analysis. Additionally, similar guidelines may be applied to determine where to arrange a product in a retail setting. Items purchased in bulk can be arranged adjacent to one another to improve sales. To suggest the products that consumers should buy, recommendation algorithms are most frequently employed in e-commerce websites like Amazon, Flipkart, etc. and streaming platforms like Netflix. Although there are numerous online and mobile apps that use recommendation engines, physical retail businesses have not yet adopted them owing to the various consequences they have, such as infrastructure, cost, etc. In this project, we've used market basket research and recommendation algorithms to develop a model that can be used in retail establishments to boost sales and improve customer satisfaction. © 2022 IEEE.

AI-ENABLED TECHNOLOGY INNOVATION IN E-COMMERCE

Recently, advanced digital/internet-based technology has become more prevalent and advanced to play a dominant role in e-commerce. Among them, AI-driven technology innovation in e-commerce plays an important role for its development. There is research potential to discuss how AI-driven technology innovation can benefit the digital economy, as typified by e-commerce, and how it can contribute to the digital transformation of companies in traditional industries. This special issue expands our understanding of organizational and customer intentions and behavior toward AI, such as privacy issues, the perceived benefits and risks of AI-driven technology innovations in e-commerce and building long-term trust relationships between users and AI.

Who's Next: Rising Star Prediction via Diffusion of User Interest in Social Networks

Finding items with potential to increase sales is of great importance in online market. In this paper, we propose to study this novel and practical problem: rising star prediction. We call these potential items Rising Star , which implies their ability to rise from low-turnover items to best-sellers in the future. Rising stars can be used to help with unfair recommendation in e-commerce platform, balance supply and demand to benefit the retailers and allocate marketing resources rationally. Although the study of rising star can bring great benefits, it also poses challenges to us. The sales trend of rising star fluctuates sharply in the short-term and exhibits more contingency caused by some external events (e.g., COVID-19 caused increasing purchase of the face mask) than other items, which cannot be solved by existing sales prediction methods. To address above challenges, in this paper, we observe that the presence of rising stars is closely correlated with the early diffusion of user interest in social networks, which is validated in the case of Taocode (an intermediary that diffuses user interest in Taobao). Thus, we propose a novel framework, RiseNet, to incorporate the user interest diffusion process with the item dynamic features to effectively predict rising stars. Specifically, we adopt a coupled mechanism to capture the dynamic interplay between items and user interest, and a special designed GNN based framework to quantify user interest. Our experimental results on large-scale real-world datasets provided by Taobao demonstrate the effectiveness of our proposed framework.

Collaborative Filtering Model of Graph Neural Network Based on Random Walk

This paper proposes a novel graph neural network recommendation method to alleviate the user cold-start problem caused by too few relevant items in personalized recommendation collaborative filtering. A deep feedforward neural network is constructed to transform the bipartite graph of user–item interactions into the spectral domain, using a random wandering method to discover potential correlation information between users and items. Then, a finite-order polynomial is used to optimize the convolution process and accelerate the convergence of the convolutional network, so that deep connections between users and items in the spectral domain can be discovered quickly. We conducted experiments on the classic dataset MovieLens-1M. The recall and precision were improved, and the results show that the method can improve the accuracy of recommendation results, tap the association information between users and items more effectively, and significantly alleviate the user cold-start problem.