Recommendations For Users Research Articles

Personalization at Scale: Big Data Technologies in Digital Content Delivery and Their Effect on Viewer Experience

This article examines the transformative impact of big data technologies on the digital content delivery industry, focusing on their application in enhancing customer experience through video streaming services. Through a comprehensive case study of a leading provider, we investigate the implementation of Hadoop and AWS Big Data product suites to optimize content delivery, personalize user recommendations, and enable dynamic ad insertion. Our findings demonstrate significant improvements in service reliability, viewer engagement, and advertising effectiveness. The article reveals how Big Data analytics facilitate real-time content optimization and personalized user experiences, increasing viewer retention and satisfaction. Furthermore, we explore the challenges and limitations encountered during implementation, providing insights into the practical applications of Big Data in the evolving landscape of digital media consumption. This article contributes to the growing body of literature on Big Data applications in media and entertainment, offering valuable insights for practitioners and researchers in digital content delivery.

Performance Evaluation on E-Commerce Recommender System based on KNN, SVD, CoClustering and Ensemble Approaches

E-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a multitude of factors, including users' prior purchases, browsing history, demographic information, and others. To forecast the interests and preferences of users, several techniques are chosen to be investigated in this study, which include Singular Value Decomposition (SVD), k-Nearest Neighbor Baseline (KNN Baseline) and CoClustering. In addition, several evaluation metrics including the fraction of concordant pairs (FCP), mean absolute error (MAE), root mean square error (RMSE) and normalized discounted cumulative gain (NDCG) will be used to assess how well different techniques work. To provide a better understanding, the outcomes produced in this study will be incorporated into a graphical user interface (GUI).

Performance Evaluation on E-Commerce Recommender System based on KNN, SVD, CoClustering and Ensemble Approaches

E-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a multitude of factors, including users' prior purchases, browsing history, demographic information, and others. To forecast the interests and preferences of users, several techniques are chosen to be investigated in this study, which include Singular Value Decomposition (SVD), k-Nearest Neighbor Baseline (KNN Baseline) and CoClustering. In addition, several evaluation metrics including the fraction of concordant pairs (FCP), mean absolute error (MAE), root mean square error (RMSE) and normalized discounted cumulative gain (NDCG) will be used to assess how well different techniques work. To provide a better understanding, the outcomes produced in this study will be incorporated into a graphical user interface (GUI).

An evaluation of the performance of stopping rules in AI-aided screening for psychological meta-analytical research.

Several AI-aided screening tools have emerged to tackle the ever-expanding body of literature. These tools employ active learning, where algorithms sort abstracts based on human feedback. However, researchers using these tools face a crucial dilemma: When should they stop screening without knowing the proportion of relevant studies? Although numerous stopping rules have been proposed to guide users in this decision, they have yet to undergo comprehensive evaluation. In this study, we evaluated the performance of three stopping rules: the knee method, a data-driven heuristic, and a prevalence estimation technique. We measured performance via sensitivity, specificity, and screening cost and explored the influence of the prevalence of relevant studies and the choice of the learning algorithm. We curated a dataset of abstract collections from meta-analyses across five psychological research domains. Our findings revealed performance differences between stopping rules regarding all performance measures and variations in the performance of stopping rules across different prevalence ratios. Moreover, despite the relatively minor impact of the learning algorithm, we found that specific combinations of stopping rules and learning algorithms were most effective for certain prevalence ratios of relevant abstracts. Based on these results, we derived practical recommendations for users of AI-aided screening tools. Furthermore, we discuss possible implications and offer suggestions for future research.

Ethical Challenges Arising from the Mapping of Mobile Phone Location Data

ABSTRACT Mobile phone location datasets offer opportunities to conduct spatial analysis at very granular levels. In most jurisdictions their collection and use are subject to legal requirements to protect the privacy of the individuals generating the data and to prevent its disclosure. However, it can be the case that geographically referenced datasets, which meet the legal and statistical requirements of non-disclosure, reveal much more when they are plotted on a map. This paper provides an overview of the particular ethical challenges posed to mapmakers by mobile phone location data, whilst keeping in mind the potential benefits of harnessing these data to inform research and public policy. We identify the principal ethical concern pertaining to the use of such location datasets: preventing disclosure and protecting privacy, but also considering the spatial and socio-political contexts that the data are collected and produced from. We then discuss case studies that highlight the context-specific considerations necessary to create ethical maps from sensitive location datasets, and offer experience-based recommendations for users considering the ethical implications of using mobile phone location data in research.

ADCMDES: Design of an augmented cross-domain collaborative recommendation model using novel distance metric and ensemble stratification

Collaborative recommendation involves leveraging information from one entity to suggest attributes of other entities through pattern analysis. These models are invaluable for understanding data behaviour’s impact on related entities. Researchers propose various pattern recognition and correlation models, employing distance metrics like Jaccard, Cosine, etc., to estimate correlations between user queries and recommendation datasets. However, these models become less efficient as dataset size increases due to exponential correlation estimation delays. To enhance scalability while retaining recommendation quality, a new approach is introduced: ADCMDES. This novel augmented cross-domain collaborative model employs a hybrid distance metric and ensemble stratification for dataset pruning. It operates semi-supervisedly, requiring information about the entities being collaborated upon. This data is used to cluster similar collaborative entities, producing a condensed cluster with greater relevancy to user queries. Utilizing word2vec, records are transformed into features for an ensemble classification engine. The resultant model categorizes user input, directing it to the most relevant cluster. Entries within this cluster are ranked using a hybrid metric amalgamating 18 distance measures, enhancing correlation between input queries and recommendations. In testing, ADCMDES exhibited 15% better accuracy, 8% better precision, 9% better recall, and 3% lower RMSE compared to standard models across datasets. While some delay is introduced due to ensemble classification and augmented feature pooling, this doesn't considerably affect long-term recommendation performance and can be mitigated through parallel processing and redundancy reduction techniques.

E-commerce and Mobile Application Development in the Sports Industry

In order to achieve user recommendations that best match their current contextual needs, the author proposes a mobile service QoS (Quality of Service) hybrid recommendation model based on sports user situational awareness. Cluster the users and service items covered by mobile users based on their location contextual information according to the classification principle of autonomous systems, forming a collaborative filtering and recommendation mechanism for mobile user service items; In response to the cold start problem of new users and new projects in traditional QoS recommendation methods, prediction and recommendation of missing QoS attribute preference values are based on User based and Item based CF; In response to the problem of difficult determination of mixed recommendation weights caused by massive data and uneven distribution of service QoS attribute values in mobile network environments, MapReduce based ant colony neural network weight training is used for CF mixed recommendation. Experimental results have shown that the Hadoop sports industry has improved the operational efficiency of algorithms and reduced the time for global QoS preference prediction; And by comparing the operation results of the 10% and 100% sub datasets, it can also be seen that the acceleration ratio of the algorithm will continuously improve with the increase of data volume, thereby improving the operational efficiency of the recommendation algorithm. It has been proven that the MapReduce based ant colony neural network weight training method significantly reduces the global computation time of the algorithm and improves the operational efficiency of the recommendation system.

Pupillometry is sensitive to speech masking during story listening: A commentary on the critical role of modeling temporal trends

An increase in pupil size is an important index of listening effort, for example, when listening to speech masked by noise. Specifically, the pupil dilates as the signal-to-noise ratio decreases. A growing body of work aims to assess listening effort under naturalistic conditions using continuous speech, such as spoken stories. However, a recent study found that pupil size was sensitive to speech masking only when listening to sentences but not under naturalistic conditions when listening to stories. The pupil typically constricts with increasing time on task during an experimental block or session, and it may be necessary to account for this temporal trend in experimental design and data analysis in paradigms using longer, continuous stimuli. In the current work, we re-analyze the previously published pupil data, taking into account a problematic constraint of randomization and time-on-task, and use the data to outline methodological solutions for accounting for temporal trends in physiological data using linear mixed models. The results show that, in contrast to the previous work, pupil size is indeed sensitive to speech masking even during continuous story listening. Furthermore, accounting for the temporal trend allowed modeling the dynamic changes in the speech masking effect on pupil size over time as the continuous story unfolded. After demonstrating the importance of accounting for temporal trends in the analysis of empirical data, we provide simulations, methodological considerations, and user recommendations for the analysis of temporal trends in experimental data using linear mixed models.

A diversity-aware recommendation system for tutoring

Diversity plays a major role when a student is looking for a tutor to better understand some concepts or an entire course. In fact, algorithms for recommending potential tutors have to take into account several aspects of diversity that may be critical to successful tutoring. The tutor must have the appropriate competencies in the subject matter. In addition, he/she must be able to convey the knowledge and skills associated with the tutoring topic. Moreover, the personality traits of the tutee and the tutor can facilitate or hinder the learning process during tutoring. This study presents the experience of ‘SOS TUTORÍA UC’, a responsive web application aimed at facilitating academic assistance among students. Special emphasis is placed on the importance of incorporating dimensions of diversity that can inform the artificial intelligence algorithms of the potential tutor recommendation system. Indeed, competence in the topic tutored is the first diversity criterion for recommending more knowledgeable potential tutors. In addition, the tutee has to choose whether to look for tutors with personalities that are “different”, “similar” or “indifferent” to his or her own preferences for academic assistance on the specific topic. To achieve this, ‘SOS TUTORÍA UC’ is integrated with the WeNet platform, which provides user management services and user recommendation algorithms. The results of the testing of the recommendation system were positive with regard to the criterion of competence, while the criterion of personality should be addressed for improvement. In order to improve the tutor-tutee matching process, participants emphasized the importance of considering the criterion of personality traits in addition to competence. They also requested additional information and parameters to facilitate tutor selection.

Ethical AI in Financial Inclusion: The Role of Algorithmic Fairness on User Satisfaction and Recommendation

This study investigates the impact of artificial intelligence (AI) on financial inclusion satisfaction and recommendation, with a focus on the ethical dimensions and perceived algorithmic fairness. Drawing upon organizational justice theory and the heuristic–systematic model, we examine how algorithm transparency, accountability, and legitimacy influence users’ perceptions of fairness and, subsequently, their satisfaction with and likelihood to recommend AI-driven financial inclusion services. Through a survey-based quantitative analysis of 675 users in China, our results reveal that perceived algorithmic fairness acts as a significant mediating factor between the ethical attributes of AI systems and the user responses. Specifically, higher levels of transparency, accountability, and legitimacy enhance users’ perceptions of fairness, which, in turn, significantly increases both their satisfaction with AI-facilitated financial inclusion services and their likelihood to recommend them. This research contributes to the literature on AI ethics by empirically demonstrating the critical role of transparent, accountable, and legitimate AI practices in fostering positive user outcomes. Moreover, it addresses a significant gap in the understanding of the ethical implications of AI in financial inclusion contexts, offering valuable insights for both researchers and practitioners in this rapidly evolving field.

Interval abstractions for robust counterfactual explanations

Counterfactual Explanations (CEs) have emerged as a major paradigm in explainable AI research, providing recourse recommendations for users affected by the decisions of machine learning models. However, CEs found by existing methods often become invalid when slight changes occur in the parameters of the model they were generated for. The literature lacks a way to provide exhaustive robustness guarantees for CEs under model changes, in that existing methods to improve CEs' robustness are mostly heuristic, and the robustness performances are evaluated empirically using only a limited number of retrained models. To bridge this gap, we propose a novel interval abstraction technique for parametric machine learning models, which allows us to obtain provable robustness guarantees for CEs under a possibly infinite set of plausible model changes Δ. Based on this idea, we formalise a robustness notion for CEs, which we call Δ-robustness, in both binary and multi-class classification settings. We present procedures to verify Δ-robustness based on Mixed Integer Linear Programming, using which we further propose algorithms to generate CEs that are Δ-robust. In an extensive empirical study involving neural networks and logistic regression models, we demonstrate the practical applicability of our approach. We discuss two strategies for determining the appropriate hyperparameters in our method, and we quantitatively benchmark CEs generated by eleven methods, highlighting the effectiveness of our algorithms in finding robust CEs.

Recommendation mobile antivirus for Android smartphones based on malware detection

<p>The proliferation of smartphone malware attacks due to a lack of vigilance in app selection raises serious concerns. Built-in smartphone security features often must be improved to protect devices from these threats. Although numerous articles recommend top-tier antivirus solutions, there need to be more reliable data sources that raise suspicions about undisclosed promotional motives. This research endeavors to establish a ranking of antivirus efficacy to provide optimal recommendations for Android smartphone users. The research methodology entails a meticulous comparison of malware detection and labeling outcomes between various antivirus programs within Virustotal and the labeling system employed by the Euphony application. The comparative results are categorized into three groups: antivirus solutions proficient in identifying specific malware types, those detecting malware presence without categorization, and antivirus software failing to detect malware effectively. The experimental findings present the five leading antivirus solutions, ranked from the highest to lowest scores, as Ikarus, Fortinet, ESET-NOD32, Avast-Mobile, and SymantecMobileInsight. Based on the comprehensive assessment conducted in this study, these solutions are recommended as the top antivirus choices. These recommendations are poised to significantly aid users in selecting the most suitable antivirus protection for their Android smartphones.</p>

Unveiling the influencing mechanism underlying users’ adoption and recommend intentions of central bank digital currency: A behavioral reasoning theory perspective

Despite the enormous benefits of the implementation of central bank digital currency (CBDC), the realization of these anticipated benefits depends on users' adoption and recommendations resulting from the consideration of both facilitators and inhibitors. Drawing on the behavioral reasoning theory (BRT), this study endeavors to unveil the influencing mechanism underlying users' CBDC adoption and recommend intentions using a hybrid approach integrating qualitative study (i.e., thematic analysis) and quantitative study (i.e., PLS-SEM). The qualitative research results indicate that relative advantage, incentive measures, government support, compatibility, and convenience are the “reasons for” users' CBDC adoption and recommend intentions, while tradition barrier, risk barrier, usage barrier, and image barrier are the “reasons against” users' CBDC adoption and recommend intentions. Further, the quantitative analysis of 638 pilot CBDC users suggests that users' CBDC adoption intentions positively influence their CBDC recommend intentions. “Reasons for” positively influence both users' attitudes and adoption intentions toward CBDC, whereas “reasons against” only negatively influence users' adoption intentions toward CBDC. Values (openness to change) positively influence users' “reasons for” and attitudes toward CBDC, and negatively influence users' “reasons against” adopting CBDC. Financial literacy strengthens the paths between “reasons for” and adoption intentions and weakens the paths between “reasons against” and adoption intentions. Theoretically, this study contributes by providing a thorough knowledge of users’ values, reasons, attitudes, and adoption and recommend intentions toward CBDC. Practically, this study renders insightful policy implications for central banks worldwide to promote CBDC applications.

Path-based and triplification approaches to mapping data into RDF: User behaviours and recommendations

Path-based and triplification approaches to mapping data into RDF: User behaviours and recommendations

Comparative analysis of the dispersion modeling and dose projection results performed under BARCO international project

Radiological assessments on zones to take protective actions in case of a nuclear or radiological emergency involve a series of real-time forecasts of radiological impact on the public at various distances from the release point, using actual weather or forecast data, information on the source term or facility status, and primary radiation monitoring data. This practice is implemented during the operation of emergency centers around the world in order to promptly report the occurrence and possible consequences of radiological accidents in the country and abroad in the event of a possible transboundary impact. Since the Chornobyl disaster, a lot of emergency exercises, research programs and projects, in particular, benchmarking, have served as international platforms for improving modeling capacity in atmospheric dispersion. This activity is carried out both on the basis of past severe accidents with significant atmospheric releases and corresponding radiological consequences, and on the basis of specific conditional (hypothetical) events that are developed in accordance with the purpose of the study. The paper is focused on the comparison results performed under the international project “Benchmarking on Assessment of Radiological COnsequences” (BARCO) conducted in 2020–2021 between five technical support organisations – members of the European Technical Safety Organisations Network (ETSON). The work contains a short overview of relevant international activity conducted in the past, a description of the BARCO project and its objectives, a list of participants, project tasks, initial data (source term, meteorology, list of benchmarking quantities, approach to data exchange, codes used). The study presents some of comparative analysis results obtained via two techniques such as code-to-code analysis (CTCA) and matched-pair analysis (MPA). The results discussion concentrates on the overall recommendations for code users. Conclusions provide the main outputs of the project.

Prediction of Hearing Help Seeking to Design a Recommendation Module of an mHealth Hearing App: Intensive Longitudinal Study of Feature Importance Assessment.

Mobile health (mHealth) solutions can improve the quality, accessibility, and equity of health services, fostering early rehabilitation. For individuals with hearing loss, mHealth apps might be designed to support the decision-making processes in auditory diagnostics and provide treatment recommendations to the user (eg, hearing aid need). For some individuals, such an mHealth app might be the first contact with a hearing diagnostic service and should motivate users with hearing loss to seek professional help in a targeted manner. However, personalizing treatment recommendations is only possible by knowing the individual's profile regarding the outcome of interest. This study aims to characterize individuals who are more or less prone to seeking professional help after the repeated use of an app-based hearing test. The goal was to derive relevant hearing-related traits and personality characteristics for personalized treatment recommendations for users of mHealth hearing solutions. In total, 185 (n=106, 57.3% female) nonaided older individuals (mean age 63.8, SD 6.6 y) with subjective hearing loss participated in a mobile study. We collected cross-sectional and longitudinal data on a comprehensive set of 83 hearing-related and psychological measures among those previously found to predict hearing help seeking. Readiness to seek help was assessed as the outcome variable at study end and after 2 months. Participants were classified into help seekers and nonseekers using several supervised machine learning algorithms (random forest, naïve Bayes, and support vector machine). The most relevant features for prediction were identified using feature importance analysis. The algorithms correctly predicted action to seek help at study end in 65.9% (122/185) to 70.3% (130/185) of cases, reaching 74.8% (98/131) classification accuracy at follow-up. Among the most important features for classification beyond hearing performance were the perceived consequences of hearing loss in daily life, attitude toward hearing aids, motivation to seek help, physical health, sensory sensitivity personality trait, neuroticism, and income. This study contributes to the identification of individual characteristics that predict help seeking in older individuals with self-reported hearing loss. Suggestions are made for their implementation in an individual-profiling algorithm and for deriving targeted recommendations in mHealth hearing apps.

Ranking English Apps From the Google Play Store Based on Customer Reviews Analysis

Nowadays, several educational technology and English learning apps on the Google Play Store cater to various learning levels and objectives. However, the parents had difficulty finding an appropriate app for their kids. It is challenging when there is a diversity of English learning apps in the market. Not only product brands but also prices affect the customer‘s choice behavior. A good product will achieve customer satisfaction. And the praise of experienced customers will be a suggestion for potential customers. This paper uses sentiment analysis as a factor of criteria set for evaluating English learning Apps from Vietnamese companies. The Python language was used in all the analysis processes, and the sentiment score of 8,756 reviews for English learning apps was calculated. The criteria to evaluate the App include overall rating, sentiment scores, and number of downloads. The results will be a suggestion for improving the quality of companies‘ products and a recommendation for users when choosing a suitable Edtech product.

“The truth should not be hidden”: Experiences and recommendations of individuals making NPE discoveries through genetic genealogy databases

PurposeFueled by direct-to-consumer (DTC) genetic testing and genetic-relative finder services, some participants in genetic genealogy databases are making “not parent expected” (NPE) discoveries. To better understand experiences of this phenomenon, we surveyed a large cohort of users of genetic relative finder (GRF) services concerning their experiences after an NPE discovery. MethodsUsing thematic analysis, we analyzed responses from a cohort of GRF users (n = 646) to open-ended survey items to understand these experiences and their recommendations for DTC genetic testing companies and other GRF users. ResultsWe found that individuals had both positive and negative emotional experiences related to the NPE discovery. Positive aspects included deeper self-understanding, connecting with new family members, and uncovering answers to questions. Negative aspects included rejection by new genetic relatives, inability to seek answers from relatives who had already died, and impairment of family relationships, especially with mothers. For many participants, the challenges after the discovery nevertheless felt worthwhile because the truth was uncovered. Perhaps notably, some participants suggested enhanced warnings prediscovery and improved support after discovery from companies who provide DTC genetic testing services. ConclusionGRF services are powerful tools for family research and genealogy. Despite some possible positive and worthwhile experiences arising from making an NPE discovery, GRF users risk dealing with this potentially life-altering experience without adequate support. Participants in this study recommended an increase in resources from DTC genetic testing companies that could help users anticipate and navigate an NPE discovery.

Future music therapy programming and research for adults with substance use disorders: A reflexive thematic analysis of service user recommendations

ABSTRACT Introduction Music therapy (MT) can be beneficial regarding craving, motivation, and treatment readiness for adults with substance use conditions (SUC). However, there is a lack of literature investigating service users’ preferences and recommendations regarding MT programming and research. Informed by the recovery movement and evidence-based practice, the purpose of this study was to understand the preferences and recommendations for future MT programming and research of adults with SUC. Method Author 1 provided 24 group recreational MT and songwriting sessions to adults with SUC in a detoxification unit. Author 2 then individually interviewed 46 participants who volunteered to share their perspectives and recommendations regarding future music therapy programming and research. We used an inductive approach to reflexive thematic analysis to analyze data and had a music therapist provide trustworthiness to ensure our findings were credible. Results We organized 12 subthemes into four major themes that described participants’ preferences and recommendations for future MT programming and research for people with SUC: Unit factors; The holistic nature of addiction; MT factors; and Research factors. Findings provided situated context regarding recommendations for MT programming, what to investigate, and how to best conduct the research. Discussion As participants had the lived experience of a SUC and had experienced MT, their recommendations can provide a framework for MT programming and different methods for how to conduct MT research. Implications, limitations, and suggestions for future research are provided.

Explicable recommendation model based on a time‐assisted knowledge graph and many‐objective optimization algorithm

SummaryExisting research on recommender systems primarily focuses on improving a single objective, such as prediction accuracy, often ignoring other crucial aspects of recommendation performance such as temporal factor, user satisfaction, and acceptance. To solve this problem, we proposed an explicable recommendation model using many‐objective optimization and a time‐assisted knowledge graph, which utilizes user interaction times within the graph to prioritize recommending recently frequently visited items and is further optimized using a many‐objective optimization algorithm. In this model, the temporal weight of user actions at different times is first determined through a time decay function. Additionally, if a user clicks on the same item again, the current action's temporal weight is set to one. This strategy prioritizes recent user actions and frequently visited items, reflecting current interests and preferences better. Next, the created knowledge graph is used to create a list of potential recommendations. Embedding methods obtain the vectors for entities and relations in the path. These vectors, combined with the temporal weight of actions, quantify the explainability of user recommendations. Optimizing the rest of the recommendation performance with many objective algorithms while focusing on the user's recent frequent visits to the item. Finally, the outcomes of the research study indicate that, compared to other explicable recommended methods, our model, considering temporal factor, improved average accuracy by 11%, diversity by 1%, and explainability by 21% in the Useraction1 data set. Results in other data sets also indicate that the proposed model maintains accuracy, diversity, and novelty while enhancing explainability.