Recommendations For Users Research Articles

Social Network Community Detection to Deal with Gray-Sheep and Cold-Start Problems in Music Recommender Systems

Information from social networks is currently being widely used in many application domains, although in the music recommendation area, its use is less common because of the limited availability of social data. However, most streaming platforms allow for establishing relationships between users that can be leveraged to address some drawbacks of recommender systems. In this work, we take advantage of the social network structure to improve recommendations for users with unusual preferences and new users, thus dealing with the gray-sheep and cold-start problems, respectively. Since collaborative filtering methods base the recommendations for a given user on the preferences of his/her most similar users, the scarcity of users with similar tastes to the gray-sheep users and the unawareness of the preferences of the new users usually lead to bad recommendations. These general problems of recommender systems are worsened in the music domain, where the popularity bias drawback is also present. In order to address these problems, we propose a user similarity metric based on the network structure as well as on user ratings. This metric significantly improves the recommendation reliability in those scenarios by capturing both homophily effects in implicit communities of users in the network and user similarity in terms of preferences.

Knowledge-based recommender systems: overview and research directions.

Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.

DIGITAL BUSINESS TRANSFORMATION: ANALYSIS OF THE EFFECT ARTIFICIAL INTELLIGENCE IN E-COMMERCE’S PRODUCT RECOMMENDATION

The purpose of this study is to determine whether artificial intelligence used in E-Commerce influences product recommendations for users. This study explains how much influence artificial intelligence on product recommendations supplied by E-commerce in terms of consumer behavior in making purchasing decisions. Research methods. This research used bibliometric analysis to find the mapping of this topic with articles period 2017 to 2023 from Scopus database. Of the 103 articles were showed by keyword and analyzing the articles according to the relate of the content about 29 articles were finally obtained. The research result is Artificial Intelligence has influence for E-commerce, recommendation system, decision support system, customer behaviour’s, and customer trust. Product recommendations have an impact on E-Commerce. Conclusion. However, from the literature review, founded that there are still a few journals discussing related to considerations to the implementation regarding the use of AI in e-commerce "Consumer behaviour", "Customer Trust", "Purchasing decisions". This study is also useful to generate additional AI-related research in e-commerce and unquestionably for a fresh subject will be covered especially in context of product recommendations on E-commerce.

A Novel Cross-Domain Recommendation with Evolution Learning

In this “info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activities and e-commerce. Several techniques have been widely applied for recommendation systems, but the cold-start and sparsity problems remain a major challenge. The cold-start problem occurs when generating recommendations for new users and items without sufficient information. Sparsity refers to the problem of having a large amount of users and items but with few transactions or interactions. In this article, a novel cross-domain recommendation model, Cross-Domain Evolution Learning Recommendation (abbreviated as CD-ELR), is developed to communicate the information from different domains in order to tackle the cold-start and sparsity issues by integrating matrix factorization and recurrent neural network. We introduce an evolutionary concept to describe the preference variation of users over time. Furthermore, several optimization methods are developed for combining the domain features for precision recommendation. Experimental results show that CD-ELR outperforms existing state-of-the-art recommendation baselines. Finally, we conduct experiments on several real-world datasets to demonstrate the practicability of the proposed CD-ELR.

Research on multi-role classification task of online mall based on heterogeneous graph neural network

With the rapid development of e-commerce, online shopping malls have become an indispensable part of daily life. In order to better meet the needs of consumers, marketplace platforms need to accurately identify and categorize different user personas to provide personalized services and recommendations. In traditional role classification methods, basic information and behavioral data of users are typically used for classification. However, this approach often ignores the complex relationships between users and multiple heterogeneous data such as goods, reviews, social networks, and more. Therefore, we propose a new approach based on heterogeneous graphs to model different types of data in the form of graphs to better capture the connections between users and various elements in the marketplace. In this study, graph embedding technology is used to map nodes in heterogeneous graphs into low-dimensional vector spaces to capture similarities and relationships between nodes. Then, using the vector representation of these nodes, we can apply algorithms such as attention mechanisms for multi-role classification. Specifically, we use algorithms such as support vector machines to train classification models and use heterogeneous graph attention mechanisms to obtain the final feature representation of nodes. Experimental results show that our method shows significant advantages in multi-role classification tasks. Finally, the results of this study are discussed and summarized. We found that the classification model based on heterogeneous graph can effectively classify multiple roles in the online mall to provide personalized services and recommendations for the mall. At the same time, we also find that the construction of heterogeneous maps and the choice of graph embedding technology have important impacts on the classification results, which need further research and optimization. Therefore, multi-role task classification of online shopping malls based on heterogeneous graph neural networks is of great significance for improving the user experience and recommendation effect of online shopping malls, and also provides new ideas and methods for research in related fields.

Improving Usability in Mobile Apps for Residential Energy Management: A Hybrid Approach Using Fuzzy Logic

This paper presents a study that evaluates the usability and user experience of a mobile application interface for residential energy management, adopting a hybrid approach that integrates quantitative and qualitative methods within a user-centered design framework. For the evaluation, metrics and tools such as the User Experience Questionnaire Short (UEQ-S) and the System Usability Scale (SUS) were used, in addition to the implementation of a fuzzy logic model to interpret and contrast the data obtained through these metrics, allowing a more accurate assessment of usability and user experience, reflecting the variability and trends in the responses. Three aspects evaluated stand out: satisfaction with the interface, ease of use, and efficiency. These are fundamental to understanding how users perceive the system. The results indicate a high likelihood of user recommendation of the system and a high overall quality of user experience. This study significantly contributes to mobile application usability, especially in residential energy management, offering valuable insights for designing more intuitive and effective user interfaces on mobile devices.

Application of SVM model based on collaborative filtering hybrid algorithm in e-commerce recommendation

ABSTRACT The current e-commerce suggestion suffers from information overload, which makes it harder for customers to choose products. Due to this, researchers created a hybrid collaborative filtering algorithm and support vector machine e-commerce recommendation model to improve user recommendations. The support vector machine for effective offline classification also explains how to pick the right label threshold, the sparsity of the dataset, and other challenges from both the user- and item-oriented perspectives. According to the experimental results, the support vector machine classification methods based on user perspective and items from the Movielens dataset had average accuracies of 0.91 and 0.88, respectively, while the corresponding average accuracies on the Jester-joke dataset were 0.74 and 0.75. Both the user- and item-based collaborative filtering algorithms on the popular goods dataset generated noticeably fewer mean absolute errors than the cold goods dataset. The average coverage and accuracy of recommendations made using an e-commerce recommendation model that incorporates support vector machines and collaborative filtering algorithms are 90.2%, 92.8%, 88.4%, 83.7%, and 80.2%, respectively. This showed that the research methodology produces positive results in e-commerce recommendation.

DEVELOPMENT OF A14C PROTOCOL AT THE LMC14 FOR THE DATING OF CULTURAL HERITAGE MATERIALS: HISTORICAL MORTARS. PARTICIPATION IN THE MODIS INTERNATIONAL INTERCOMPARISON CAMPAIGN

ABSTRACTThe absolute dating of mortar by accelerator mass spectrometry (AMS) has been the subject of renewed interest for several years. International intercomparison campaigns, called MODIS (MOrtar Dating Intercomparison Study), have been carried out. The first MODIS-1 campaign highlighted limitations in mortar dating, due to the similarity between the primary material to be dated (binder) and the contaminant (exogenous CaCO3). Methods have since emerged to overcome this problem and the need for a good preliminary characterization has been proven. The Laboratoire de Mesure du Carbone 14 (LMC14) took part in the second intercomparison campaign, MODIS2, by applying thermal decomposition increments to distinguish the carbonated binder, the organic matter contaminants (late in formation pyrogenic carbonate, LDH) and limestone. The LMC14 results on MODIS2 are quite conclusive on “pure” re-carbonated lime mortar binders containing little contaminant geological limestone but show their weaknesses for mortars heavily contaminated in Dolomites, which are difficult to discern from the binder. Recommendations for users of radiocarbon (14C) dating on mortar-based materials are made in the conclusion.

Personalized Sports Health Recommendation System Assisted by Q-Learning Algorithm

In response to the current problem of single sports plan and lack of long-term motivation in recommendation systems, a more intelligent personalized sports health recommendation system was designed by introducing Q-Learning (Quality Learning) algorithm. Firstly, user sports health data was collected, and the user model was constructed to track user sport preferences and historical behavior. Secondly, the sports environment was defined, including different types of sports activities, venues, and weather. Then, the reward function was formulated to reward and punish users based on their sports activities and goals, in order to maximize long-term health benefits. Finally, the Q-Learning algorithm was implemented to continuously iteratively learn and optimize user recommendation models to provide the best personalized sports recommendations. For personalized accuracy, indicators such as precision, recall, F1 value, MAE (Mean Absolute Error), and RMSE (Root Mean Square Error) were used to evaluate, while the system’s participation in sports, user satisfaction, long-term incentive effects, and overall health improvement were collected. The results showed that the average precision of the recommendation system on 10 different datasets was 88%, and the average AUC (Area Under Curve) was 96%, which was 6.7% higher than the SVD (Singular Value Decomposition) algorithm. The user’s sports persistence rate was improved by 25%, and the health score was improved by about 13.3%. These data not only reflect the superior performance of the recommendation system but also highlight its positive impact on long-term user motivation and overall health levels. The results indicate that the proposed personalized exercise health recommendation system, assisted by the Q-Learning algorithm, has significantly improved accuracy. Moreover, it offers users more intelligent and personalized exercise suggestions, effectively increasing long-term participation in physical activities and overall health levels.

Research on the Application of Music Technology in Modern Music Education in the Information Age

Abstract In the age of information technology, the way of music teaching as a whole has changed, and music technology has brought great impetus to modern music education. The purpose of this paper is to research music teaching and musicians’ paper-and-pencil music-making habits. Firstly, based on the joint hidden semantic model and convolutional neural network, we make full use of the decisive advantage of a deep neural network to extract features automatically, obtain higher-level music feature representation from audio content, meanwhile abstract the historical behavioral information of users’ interaction with music into scoring data and integrate it into the recommender system to generate reasonable music recommendation for target users, and realize note recognition through convolutional neural network. After verifying and analyzing the effects of note recognition and music recommendation models, they are used in music teaching. The results show that the model’s recognition of musical notes is more satisfactory, and the music recommendation accuracy is improved by 6.45%~10% compared with the comparison algorithm, and it still has a specific predictive recommendation ability in a cold-start environment. The music performance of the experimental class that uses the model presented in this paper to assist in music teaching has improved by 5.08%. The note recognition and music recommendation model can be introduced into music teaching to promote the innovation and development of music teaching concepts and teaching methods and improve the effectiveness of music teaching.

MoNetExplorer: A Visual Analytics System for Analyzing Dynamic Networks With Temporal Network Motifs.

Partitioning a dynamic network into subsets (i.e., snapshots) based on disjoint time intervals is a widely used technique for understanding how structural patterns of the network evolve. However, selecting an appropriate time window (i.e., slicing a dynamic network into snapshots) is challenging and time-consuming, often involving a trial-and-error approach to investigating underlying structural patterns. To address this challenge, we present MoNetExplorer, a novel interactive visual analytics system that leverages temporal network motifs to provide recommendations for window sizes and support users in visually comparing different slicing results. MoNetExplorer provides a comprehensive analysis based on window size, including (1) a temporal overview to identify the structural information, (2) temporal network motif composition, and (3) node-link-diagram-based details to enable users to identify and understand structural patterns at various temporal resolutions. To demonstrate the effectiveness of our system, we conducted a case study with network researchers using two real-world dynamic network datasets. Our case studies show that the system effectively supports users to gain valuable insights into the temporal and structural aspects of dynamic networks.

Effective data visualization strategies in untargeted metabolomics.

Covering: 2014 to 2023 for metabolomics, 2002 to 2023 for information visualizationLC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly ad hoc fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results.

Social Media And Misleading Information In A Democracy A Mechanism Design Approach

The advent of social media platforms in modern democratic societies has fundamentally transformed how information is disseminated and consumed. These platforms have created unprecedented opportunities for communication and self-expression. However, they have also become fertile grounds for the rapid spread of inaccurate and deceptive information. Addressing this issue involves utilizing a mechanism design approach to scrutinize the impact of social media on democratic processes and propose solutions to curb the spread of misinformation. By analyzing the incentives and strategic behaviors of platform users, content creators, and information consumers, this research aims to understand how social media algorithms, user interactions, and network structures contribute to the proliferation of misleading information. The insights gained from this analysis will significantly contribute to the ongoing discourse about the role of social media in democratic societies. Furthermore, the findings will provide actionable recommendations for policymakers, platform designers, and users. The ultimate goal is to cultivate a healthier and more reliable information ecosystem, thus enhancing the quality of democratic engagement and decision-making

News Recommendation System Based on User Interest and Deep Network

In order to provide the personalized news recommendation for users more efficiently, the personalized recommendation system combined with deep network was investigated. Based on the deep network, a news recommendation system for users was designed. By means of neural network and aggregating users’ interest characteristics, users’ personalized needs for news recommendation was met. In order to solve the problem of inaccurate construction of users’ interest preference characteristics, a personalized news method based on users’ search records and interest preference was proposed. By constructing exogenous user interest preferences from user search records, the final recommendation list was generated by using the fusion method of the two preferences. Because the traditional recommendation algorithm ignored time series of the users’ browsing behaviors, an improved circulation model of neural network algorithm was proposed. The parallel convolution neural network based on attention was used to aggregate characteristics of users’ interest and the recursive neural network based on attention mechanism was used to explore hidden time series characteristics. At the same time, it was tested on real news data sets and the results showed that this method had a good recommendation effect.

SMONAC: Supervised Multiobjective Negative Actor-Critic for Sequential Recommendation.

Recent research shows that the sole accuracy metric may lead to the homogeneous and repetitive recommendations for users and affect the long-term user engagement. Multiobjective reinforcement learning (RL) is a promising method to achieve a good balance in multiple objectives, including accuracy, diversity, and novelty. However, it has two deficiencies: neglecting the updating of negative action Q values and limited regulation from the RL Q-networks to the (self-)supervised learning recommendation network. To address these disadvantages, we develop the supervised multiobjective negative actor-critic (SMONAC) algorithm, which includes a negative action update mechanism and multiobjective actor-critic mechanism. For the negative action update mechanism, several negative actions are randomly sampled during each time updating, and then, the offline RL approach is utilized to learn their Q values. For the multiobjective actor-critic mechanism, accuracy, diversity, and novelty Q values are integrated into the scalarized Q value, which is used to criticize the supervised learning recommendation network. The comparative experiments are conducted on two real-world datasets, and the results demonstrate that the developed SMONAC achieves tremendous performance promotion, especially for the metrics of diversity and novelty.

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

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

Нові виклики для функціонування активних перманентних ГНСС-станцій під час бойових дій на прикладі України

The study presents the current state of GNSS Continuously Operating Reference Stations (CORS) networks and their operational characteristics during the ongoing hostilities in Ukraine. Stable GNSS CORS network operation is crucial not only for agricultural, geodetic, and land management tasks but also for military navigation and topography. The aim of this work is to analyze the impact of hostilities in Ukraine's GNSS network, considering factors like temporary occupation of certain territories, power outages due to missile strikes on energy infrastructure, and GNSS signal jamming using radio-electronic methods in front-line regions. Another objective of this study is to highlight examples of incorrect RTK or VRS operation due to potential errors from radio-electronic jamming or GPS spoofing as well as to provide practical recommendations for surveyors. As a result, the research has analyzed changes in the number of properly functioning GNSS stations from 2021 to 2023 using the GeoTerrace and System.NET networks. These networks cover all regions of Ukraine except the temporarily occupied territories by russia. Daily processing of RINEX files with a sampling interval of 30 seconds from CORS GNSS stations was conducted using the Bernese GNSS v.5.2 software package over three years. It was noted that following the large-scale invasion in February 2022 and through the spring of that year, there was a sharp reduction of about 10% in the number of properly functioning active GNSS stations. Scientific novelty and practical importance. The article presents practical recommendations for users, such as surveyors and land managers, performing GNSS measurements in RTK or VRS modes using permanent stations, to assess the influence of radio-electronic jamming or GPS spoofing on observations. CORS network assessment and daily calculated coordinates of GNSS stations from 2021 to 2023 can be used for future geodynamic research in the region.

Collaborative Fair-is-Better Filtering for Implicit Feedback

With the wide adoption of recommender systems, fairness has increasingly become a critical topic in many applications, such as e-commerce, job search, and online entertainment. Collaborative filtering is susceptible to unfair recommendations for users from sensitive groups due to the non-negligible presence of biases. Whereas recent work mostly concerned fairness with the explicit ratings, we propose fairness-awareness recommendation models, referred to as CFBF, for implicit feedback (e.g., clicks, views, or purchases), which are ubiquitous in today’s context. This paper considers sensitive attributes, such as gender and age, in both disjoined and combined manners to investigate the models’ unfairness. We discuss several fairness metrics for implicit feedback recommendations based on Spearman’s rank correlation and Kendal Tau. Comprehensive experiments on Movielens and LastFM show that CFBF significantly improves user groups’ fairness with comparable and even better ranking performance.

A social network security user recommendation algorithm based on community user emotions

A social network security user recommendation algorithm based on community user emotions

An E-Commerce Personalized Recommendation Algorithm Based on Multiple Social Relationships

Environmental e-commerce is a sustainability-oriented e-commerce model. To address the problem of data sparsity and the lack of diversity in traditional e-commerce recommendation algorithms, a new collaborative filtering recommendation algorithm based on multiple social relationships is proposed in environmental e-commerce. In real social networks, there were many relationships between users. On the basis of the traditional matrix decomposition model, the proposed algorithm integrates multiple social relationships between users into the user feature matrix, and then the multiple social relationships between users and the user rating preference similarity were used to jointly predict the user’s rating value for commodity, thus the personalized recommendation for users was achieved. In order to verify the superiority of the proposed algorithm, in this paper, two open datasets were used to compare the performance of several recommendation algorithms. The experimental results show that compared with the traditional social recommendation algorithms, the proposed algorithm improves recommendation accuracy and diversity. In real environmental e-commerce recommendation systems, the proposed algorithm can provide users with more personalized recommendation results, and reduce the arbitrariness of customer purchases and frequent returns in reality.