Readwell: A Book Recommender Android Application with a Point-of-Sales System

The COVID-19 pandemic has given people much free time. With this, the researchers want to encourage these people to read instead of scrolling through social media. A barrier to reading for many people is not knowing what to read and disinterest in popular books that they would find when they search online. The existing websites that encourage book reading rely on social networking for their recommendations, while the collaborative filtering algorithms applied to books do not exist in the mobile application form. Readwell is a book recommender Android app with a Point-of-Sales System created using Java, Python, and SQLite databases. The information regarding the books was web scraped from the Goodreads website. It aims to apply the more efficient collaborative filtering algorithm to an accessible mobile application that allows users to directly buy the books they are interested in, thus encouraging the reading and buying of books. The researchers created unit test cases to validate the different functionalities of the application. © 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.

Matchmaking for Mental Well-being: Development of a Peer-based Support System (Peer2S) for Students during COVID Lockdown

Social support is known to be a critical factor for mental well-being. More specifically, the protective effect of quality social support in times of crisis is well documented in many psychological studies. In this study, we developed a social support matching system that connects people who are going through similar life circumstances to provide peer-based support, allowing them to better cope with their situation together. As a case study, we focused on Japanese students whose lives were impacted by the COVID-19 lock down. To develop the recommendation model used in our system, 50 participants were asked to register their profile and afterwards, 20 users determined whether they would match with each of the profiles resulting in 1000 data points. We then experimented with various collaborative filtering and deep learning approaches and evaluated their effectiveness in recommending profiles to users. Finally, a user experiment study was conducted in which 11 users used the system 2 weeks. The results showed that while there was no significant difference in perceived social support, users reported significantly less anxiety and a borderline reduction in depression. © 2023 Owner/Author.

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.

Big Data Analytics Based Recommender System for Tele-Communication Industry

In recent times, the amount of data sent and received through wireless networks has grown quickly. Smartphones and the growth of Internet access around the world are two big reasons for this volume. Due to the current state of global health, which is mostly caused by Covid-19, telecommunications companies have a great chance to find new ways to make money by using Big Data Analytics (BDA) solutions. This is because data traffic has gone up. After all, more customers are using telecommunications services. As most of the world's data is now made by smartphones and sent through the telecom network, telecom operators are facing an information explosion that makes it harder to make decisions based on the data they need to predict how people will act. This problem was solved by making a system that sorts through information and makes suggestions based on how people have behaved in the past. Content-based filtering, collaborative filtering, and a hybrid approach are the three main ways that recommender systems filter data to solve the problem of too much data and give users relevant recommendations based on their interests and the data that is being created in real-time. Distance algorithms like Cosine, Euclidean, Manhattan, and Minkowski are at the heart of the suggested recommender system, which aims to research and design an effective recommendation strategy. The suggested model suggests different telecom packages to meet the needs of users to increase revenue per subscriber and get consumers, telecom providers, and corporations to sign long-term contracts. © 2022 IEEE.

Therapeutic drug combinations against COVID-19 obtained by employing a collaborative filtering method.

The outbreak of coronavirus disease 2019 (COVID-19) has severely harmed human society and health. Because there is currently no specific drug for the treatment and prevention of COVID-19, we used a collaborative filtering algorithm to predict which traditional Chinese medicines (TCMs) would be effective in combination for the prevention and treatment of COVID-19. First, we performed drug screening based on the receptor structure prediction method, molecular docking using q-vina to measure the binding ability of TCMs, TCM formulas, and neo-coronavirus proteins, and then performed synergistic filtering based on Laplace matrix calculations to predict potentially effective TCM formulas. Combining the results of molecular docking and synergistic filtering, the new recommended formulas were analyzed by reviewing data platforms or tools such as PubMed, Herbnet, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the Guide to the Dispensing of Medicines for Clinical Evidence, and the Dictionary of Chinese Medicine Formulas, as well as medical experts' treatment consensus in terms of herbal efficacy, modern pharmacological studies, and clinical identification and typing of COVID-19 pneumonia, to determine the recommended solutions. We found that the therapeutic effect of a combination of six TCM formulas on the COVID-19 virus is the result of the overall effect of the formula rather than that of specific components of the formula. Based on this, we recommend a formula similar to that of Jinhua Qinggan Granules for the treatment of COVID-19 pneumonia. This study may provide new ideas and new methods for future clinical research. Classification: Biological Science.

A new sentiment analysis method to detect and Analyse sentiments of Covid-19 moroccan tweets using a recommender approach.

Since the beginning of the covid-19 crisis, people from all over the world have used social media platforms to publish their opinions, sentiments, and ideas about the coronavirus epidemic and their news. Due to the nature of social networks, users share an immense amount of data every day in a freeway, which gives them the possibility to express opinions and sentiments about the coronavirus pandemic regardless of the time and the place. Moreover, The rapid number of exponential cases globally has become the apprehension of panic, fear, and anxiety among people. In this paper, we propose a new sentiment analysis approach to detect sentiments in Moroccan tweets related to covid-19 from March to October 2020. The proposed model is a recommender approach using the advantages of recommendation systems for classifying each tweet into three classes: positive, negative, or neutral. Experimental results show that our method gives good accuracy(86%) and outperforms the well-known machine learning algorithms. We find also that the sentiments of users changed from period to period, and that the evolution of the epidemiological situation in morocco affects the sentiments of users.

THE EFFECT OF CONTEXTUAL INFORMATION AS AN ADDITIONAL FEATURE IN THE RECOMMENDATION SYSTEM

This study discusses the use of an online learning recommendation system as a smart solution related to changing the face-to-face learning process to online. This study uses user-based collaborative filtering, item-based collaborative filtering, and hybrid collaborative filtering. This research was conducted in two stages using the KNN machine learning algorithm: (1) the three methods were tested to obtain student grade prediction results without adding contextual information, and (2) with the same method the same steps were carried out but with the addition of contextual information features as a feature addition. One of the alternatives carried out in this study is related to the possibility of predicting student grades. This study proves that the use of contextual information as an additional feature in the recommendation system has a significant effect on the accuracy of student score prediction results, which are used as the basis for providing recommendations using the rule base technique. © 2023, Grand Canyon University. All rights reserved.

E-learning recommender system dataset.

Mandarine Academy is an Ed-Tech company that specializes in innovative corporate training techniques such as personalized Massive Open Online Courses (MOOCs), web conferences, etc. With more than 550K users spread across 100 active e-learning platforms. The company creates online pedagogical content (videos, quizzes, documents, etc.) on daily basis to support the digitization of work environments and to keep up with current trends. Mandarine Academy provided us with access to Mooc.office365-training.com. A publicly available MOOC in both French and English versions to conduct research on recommender systems in online learning environments. Mandarine Academy collects user feedback using two types of ratings: Explicit (Like Button, Social share, Bookmarks), and Implicit (Watch Time, Page View). Unfortunately, explicit ratings are underutilized. Most users avoid the burden of stating their preferences explicitly. To address this, we shift our attention to implicit interactions, which generate more data that can be significant in some cases. Implicit Ratings are what constitute Mandarine Academy Recommender System (MARS) Dataset. We believe that the degree of viewing has an impact on the overall impression, for this reason, we applied changes to the implicit data and made a part of it similar to the explicit rating format found in other known datasets (e.g., Movielens). This paper presents two real-world dataset variations that consist of 89,000 explicit ratings and 276,000 implicit ratings. Data was collected starting early 2016 until late 2021. Chosen users had rated at least one item. To protect their privacy, sensitive information has been removed. To the best of our knowledge, this is the first publicly available real-world dataset of E-Learning recommendations in both French and English with mixed ratings (implicit and explicit), allowing the research community to focus on pre-and post-COVID-19 behavior in online learning.

Clustering-Based Recommendation System for Preliminary Disease Detection

The catastrophic outbreak COVID-19 has brought threat to the society and also placed severe stress on the healthcare systems worldwide. Different segments of society are contributing to their best effort to curb the spread of COVID-19. As a part of this contribution, in this research, a clustering-based recommender system is proposed for early detection of COVID-19 based on the symptoms of an individual. For this, the suspected patient's symptoms are compared with the patient who has already contracted COVID-19 by computing similarity between symptoms. Based on this, the suspected person is classified into either of the three risk categories: high, medium, and low. This is not a confirmed test but only a mechanism to alert the suspected patient. The accuracy of the algorithm is more than 85%.

Coffee Shop Recommendation System Using an Item-Based Collaborative Filtering Approach

To inhibit the rate of transmission of the Covid-19 virus, one of the efforts made by the Indonesian government is to impose a system of limiting social activities. Thus, resulting in changes in patterns and lifestyles in a short time. Including this 'Coffee' activity. A large amount of time available due to WFH has also resulted in an increase in the number of coffee connoisseurs, including the existence of the coffee shop itself. This makes it difficult for coffee fans to choose which coffee shop is the right one to go to desire. So, a recommendation system is needed that aims to provide advice on which coffee shop to choose. The recommendation system is a system that helps users overcome overflowing information by providing specific recommendations for users and it is hoped that these recommendations can meet the wishes and needs of users. There are three types of recommendation systems based on the methods they use, namely collaborative filtering, content-based filtering, and hybrid. The method used is collaborative filtering is often used in recommendation systems. Collaborative filtering is divided into two parts, namely Item-based collaborative filtering and User-based collaborative filtering. This paper uses Item-based collaborative filtering which uses rating data between users to get recommendations. In this technique, each coffee shop that is rated by the user is checked with similar coffee shops, then combines these similar coffee shops into a list of recommendations. The test results show that the Item-based collaborative filtering method with an adjusted cosine similarity algorithm can display recommendations that are by the rating given by the customer. © 2022 IEEE.

Collaborative and Content-Based Filtering Hybrid Method on Tourism Recommender System to Promote Less Explored Areas

The COVID-19 pandemic has significantly impacted various areas of life, including tourism. Currently, the tourism sector is starting to recover and start its activities. However, several tourist attractions have not been explored, thus making visitors less aware of information about these tours. This affects the number of tourist visits. Therefore, there is a need of an information technology approach to promote tourism objects, including a tourist recommendation system. This study proposed a hybrid recommendation system incorporating collaborative and content based filtering. This model is proven to be able to produce good rating predictions on a recommendation system. This hybrid method uses a linear combination by calculating the rating matrix and user profile as the first step in providing rating predictions. Collaborative filtering is calculated using the cosine similarity algorithm and weighted sum algorithm, while the content-based filtering method is performed by calculating the weight of each available feature. We apply this model to the Palembang tourism dataset to the the website. This system recommends existing historical tourist attractions based on visitor criteria. The results show the existing data's effective, efficient, and accurate results. The calculation result that the rating prediction using the hybrid method is 3.203. In addition, this method can also help overcome existing cold start problems. © Roman Science Publications Inc.

An Efficient Algorithm to Recommend Covid-19 Related Solutions

With the rise of Covid-19, the open-source community has devoted a huge amount of time into developing technical solutions to stop the spread of the virus. Useful solutions like symptom trackers and extensive analysis on existing datasets are a small drop in the massive number of solutions developed by people. But with the massive number of projects or solutions, it is time consuming for a motivated person to find an appropriate solution to put his time into. Therefore, seeing the inspiring amount of work done by the open source community, we are suggesting an efficient algorithm to recommend projects that are Coronavirus related to which the user can get recommendations for projects according to their preference such as language. © 2022 IEEE.

DCA-IoMT: Knowledge-Graph-Embedding-Enhanced Deep Collaborative Alert Recommendation Against COVID-19

Filtration to optimal exactness is mandatory since the options inundate the online world. Knowledge graph embedding is extraordinarily contributing to the recommendations, but the existing knowledge graph (KG)-based recommendation methods only exploit the correlations among the preferences and stand-alone entities, without bonding the cocurricular features and tendencies of the context. Additionally, the integration of the location-based current data of coronavirus disease 2019 (COVID-19) into the KG is necessary for the recommendation of region-aware precautionary alerts to the concerned people-an essential application of the current and future Internet of Medical Things. Therefore, in this article, we propose a novel deep collaborative alert recommendation (DCA) approach to cope with the situation. Particularly, DCA collects current online data about COVID-19, purifies, and transforms them to the KG. Furthermore, it independently encapsulates the cocurricular features and tendencies of the context in the embedding space and encodes them to the independent hidden factors via a graph neural network. The bi-end hidden factors are computed via matrix factorization to infer the potential connections. Moreover, a relevance estimator and a cross transistor are configured to enhance the generalization capability of the model. Experiments on two real-world datasets are performed to evaluate the effectiveness of DCA. Results and analysis show that the proposed approach has outperformed the baseline methods with fine improvements in providing the required recommendations.

A Hybrid Recommender Model based on Information Retrieval for Mexican Tourism Text in Rest-Mex 2022

Nowadays, the tourism is a principal economic sector for the world due to the exportations are improved, the jobs number is enhanced and the economic is developed. In México, the tourism represents 8.7% of GDP and generates 4.5 million direct jobs, however this economic sector has been affected by COVID-19 pandemic. For these reasons, a hybrid recommender model based on information retrieval is presented in this research to tackle the recommendation systems task of Rest-Mex 2022. A vector space model with tf-idf weighting scheme and cosine similarity is implemented. Besides, a hybrid recommender model is generated applying the recommendation techniques item-item collaborative filtering, content-based filtering and switching hybrid approach. Finally, our proposal won the second and third place in the competition. © 2022 Copyright for this paper by its for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

A Hybrid Recommender System for Improving Rating Prediction of Movie Recommendation

Because of COVID-19 pandemic, online movies are now extremely popular. While the movie theaters have not serviced and people are staying quarantine, movies are the best choice for relaxing and treating stress. In present, recommender systems are widely integrated into many platforms of movie applications. A hybrid recommender system is one promising technique to improve the system performance, especially for cold-start, data sparsity, and scalability. This paper proposed a hybrid of matrix factorization, biased matrix factorization, and factor wise matrix factorization to solve all mentioned drawback problems. Simulation shows that the proposed hybrid algorithm can decrease approximately 11.91% and 10.70% for RMSE and MAE, respectively, when compared with the traditional methods. In addition, the proposed algorithm is capable of scalability. While the number of datasets is tremendously increased by 10 times, it is still effectively executed. © 2022 IEEE.

Research on Students' Course Selection Preference based on Collaborative Filtering Algorithm

Due to the events caused by the COVID-19 pandemic, the education industry is no longer limited to offline, and online classroom education is widely used. The rapid development of online education provides users with more abundant educational course resources and flexible learning methods. Various online education platforms are also constantly improving their service models to give users a better learning experience. However, at present, there are few personalized information recommendation services in student course selection. Students receive the same course selection information and cannot be "tailored" according to their specific preferences. This paper focuses on the integration of collaborative filtering technology into a college course selection system to construct a rating matrix based on students' ratings of the courses they take through correlation between courses and correlation between students. Based on the collaborative filtering algorithm, a predictive rating matrix is generated to produce a recommendation list to achieve intelligent recommendation of suitable courses for students. The experimental results show that, based on the traditional collaborative filtering recommendation technique, the improved collaborative filtering algorithm based on both item and user weighting is used to achieve course recommendation with higher recommendation accuracy. The application of the improved collaborative filtering technique in the course selection recommendation system of colleges and universities is very good at recommending courses for students intelligently, and the recommended courses for students have good rationality and accuracy, and achieve more intelligent course selection for students, which has great practicality and practical significance.

Evaluation of Herd Behavior Caused by Population-scale Concept Drift in Collaborative Filtering

Concept drift in stream data has been well studied in machine learning applications. In the field of recommender systems, this issue is also widely observed, as known as temporal dynamics in user behavior. Furthermore, in the context of COVID-19 pandemic related contingencies, people shift their behavior patterns extremely and tend to imitate others' opinions. The changes in user behavior may not be always rational. Thus, irrational behavior may impair the knowledge learned by the algorithm. It can cause herd effects and aggravate the popularity bias in recommender systems due to the irrational behavior of users. However, related research usually pays attention to the concept drift of individuals and overlooks the synergistic effect among users in the same social group. We conduct a study on user behavior to detect the collaborative concept drifts among users. Also, we empirically study the increase of experience of individuals can weaken herding effects. Our results suggest the CF models are highly impacted by the herd behavior and our findings could provide useful implications for the design of future recommender algorithms. © 2022 ACM.

Prediction of User Overall Gratification in Indian Tourism Domain on Hotel Classes and Trip-Types

The revenue and economy of the country in the past years significantly depend on tourism. The hotel sector's role is even more prominent in tourism. The plans and decisions of tours of users can be recommended with the collaboration of E-commerce and hotel management. The traveling proportion of the population is getting minor over the months due to the worst impact of COVID-19. Thus not just the tourism, the hotel sector is also in vain in terms of revenue. Users' past experiences and opinions help boost their satisfaction levels by providing recommendations and retaining them. The present scenario and stats prove that the selection and decision of hotels have enormous support on user reviews. This research article tries to find and analyze the various aspects that contribute more towards the gratification levels of users in Indian top tourism city hotels listed by the Master and VISA Inc survey. This survey focuses on the item-item collaborative filtering and regression techniques based on TripAdvisor reviews of recent times. Once the dimensions are known, it helps in improving them and thus even enhances the ratings of Asian continental hotel management. This study proves that the online travel platform helps obtain reviews from users to maintain the travel recommender systems. © The Electrochemical Society

Research on Students' Course Selection Preference based on Collaborative Filtering Algorithm

Due to the events caused by the COVID-19 pandemic, the education industry is no longer limited to offline, and online classroom education is widely used. The rapid development of online education provides users with more abundant educational course resources and flexible learning methods. Various online education platforms are also constantly improving their service models to give users a better learning experience. However, at present, there are few personalized information recommendation services in student course selection. Students receive the same course selection information and cannot be "tailored" according to their specific preferences. This paper focuses on the integration of collaborative filtering technology into a college course selection system to construct a rating matrix based on students' ratings of the courses they take through correlation between courses and correlation between students. Based on the collaborative filtering algorithm, a predictive rating matrix is generated to produce a recommendation list to achieve intelligent recommendation of suitable courses for students. The experimental results show that, based on the traditional collaborative filtering recommendation technique, the improved collaborative filtering algorithm based on both item and user weighting is used to achieve course recommendation with higher recommendation accuracy. The application of the improved collaborative filtering technique in the course selection recommendation system of colleges and universities is very good at recommending courses for students intelligently, and the recommended courses for students have good rationality and accuracy, and achieve more intelligent course selection for students, which has great practicality and practical significance.