Application Of Recommender Systems Research Articles

A Study on the Impact of Quality and HCI Factors of a Deep Learning-Based Makeup Recommendation System on Acceptance Intention: Focusing on the Mediating Effects of Users’ Perceived Trust and Immersion

This study analyzed the impact of system quality and HCI factors on user acceptance intention, mediated by perceived trust and immersion, in the context of a deep learning-based makeup recommendation system. The results indicate that system quality and HCI factors significantly influence user acceptance intention through perceived trust and immersion. This underscores the importance of system quality and HCI factors for the successful application of makeup recommendation systems. In particular, factors such as diversity, accuracy, anthropomorphism, and realism enhance user trust and immersion, leading to higher acceptance intention. However, it was found that certain factors, such as personalization, do not significantly influence acceptance intention through immersion. This suggests that the impact of specific factors should be considered in the design of recommendation systems. The study is limited by its focus on a specific age group and gender in the survey, which may restrict the generalization of the results. Additionally, there is a lack of research on users with different cultural backgrounds, necessitating an understanding of the global market applicability. Future research should extend to users with diverse demographic characteristics to derive universal design principles for makeup recommendation systems. Furthermore, the impact of factors such as personalization on user experience should be explored in depth to enhance user-centered, customized beauty services. This research provides a theoretical and practical foundation for the effective design and implementation of makeup recommendation systems and is expected to contribute to the digital transformation of the beauty industry.

Recommender Systems Applications: Data Sources, Features, and Challenges

In recent years, there has been growing interest in recommendation systems, which is matched by their widespread adoption across various sectors. This can be attributed to their effectiveness in reducing an avalanche of data into individualized information that is meaningful, relevant, and can easily be absorbed by a single person. Several studies have recently navigated the landscape of recommendation systems, attending to their approaches, challenges, and applications, as well as the evaluation metrics necessary for effective implementation. This systematic review investigates the understudied aspects of recommendation systems, including the data input into the systems and their features or outputs. The data in (input) and data out (features) are both diverse and vary significantly from not just one application domain to another, but also from one application use case to another, which is a distinction that has not been thoroughly addressed in the past. In addition, this study explores several application domains, providing a comprehensive breakdown of the categorical data consumed by these systems and the features, or outputs, of these systems. Without focusing on any particular journals or their rankings, this study collects and reviews articles on recommendation systems published from 2018 to April 2024, in four top-tier research repositories, including IEEE Xplore Digital Library, Springer Link, ACM Digital Library, and Google Scholar.

Design and Application of Intelligent Recommender Systems in Vocational Rehabilitation

With the rapid development of information technology, the application of intelligent recommender systems in various fields has become more and more extensive, and vocational rehabilitation, as a field focusing on personalized services, faces the challenge of how to effectively use big data and intelligent algorithms to enhance the rehabilitation effect. This paper discusses the design principles and key technologies of intelligent recommender system in vocational rehabilitation, combining data-driven recommendation algorithms with users' personalized needs to achieve accurate recommendation of vocational rehabilitation programs. Through the optimization of system architecture and functional implementation, the intelligent recommender system shows remarkable effects in practical application, effectively improving the decision-making efficiency and user satisfaction in the rehabilitation process. Finally, this paper gives an outlook on the future development of the intelligent recommendation system in the field of vocational rehabilitation, and proposes possible research directions and application expansion paths.

Denoising Alignment with Large Language Model for Recommendation

The mainstream approach of GNN-based recommendation aggregates high-order ID information associated with the node in the user-item graph. The aggregation pattern using ID as signal has two disadvantages: lack of textual semantics and the impact of interaction noise. These disadvantages pose a threat to effectively learn user preferences, especially in capturing intricate user-item semantic relationships. Although large language models (LLMs) allow the integration of rich textual information into recommenders and have had groundbreaking applications in recommender systems, current works need to bridge the gap between different representation spaces. This is because LLMs-based methods align the representations of GNN-based models only by using text embedding of LLM, leading to unsatisfactory results. To address this challenge, we propose a D enoising A lignment framework with L LMs for GNN-based R ecommenders (DALR), which aims to align structural representation with textual representation and mitigate the effects of noise. Specifically, We propose a modeling framework that integrates the representation of graph structure with textual information from LLMs to capture intricate user-item interactions. We also suggest an alignment paradigm to enhance representation performance by aligning semantic signals from LLMs and structural features from GNN models. Additionally, we introduce a contrastive learning scheme to relieve the impact of noise and improve model performance. Extensive experiments on public datasets demonstrate that our model consistently outperforms the state-of-the-art methods. DALR achieves improvements ranging from 2.82% to 12.20% in Recall@5 and from 1.04% to 3.48% in NDCG@5 compared to the strongest baseline model, using the Steam dataset as an example.

The development and application of a novel E-commerce recommendation system used in electric power B2B sector.

The advent of the digital era has transformed E-commerce platforms into critical tools for industry, yet traditional recommendation systems often fall short in the specialized context of the electric power industry. These systems typically struggle with the industry's unique challenges, such as infrequent and high-stakes transactions, prolonged decision-making processes, and sparse data. This research has developed a novel recommendation engine tailored to these specific conditions, such as to handle the low frequency and long cycle nature of Business-to-Business (B2B) transactions. This approach includes algorithmic enhancements to better process and interpret the limited data available, and data pre-processing techniques designed to enrich the sparse datasets characteristic of this industry. This research also introduces a methodological innovation that integrates multi-dimensional data, combining user E-commerce activities, product specifics, and essential non-tendering information. The proposed engine employs advanced machine learning techniques to provide more accurate and relevant recommendations. The results demonstrate a marked improvement over traditional models, offering a more robust and effective tool for facilitating B2B transactions in the electric power industry. This research not only addresses the sector's unique challenges but also provides a blueprint for adapting recommendation systems to other industries with similar B2B characteristics.

APPLYING CLUSTERING AND RECOMMENDATION SYSTEM FOR EFFECTIVE SUPERVISION IN CENTRAL KALIMANTAN INSPECTORATE

This study aims to enhance the effectiveness of supervision at the Central Kalimantan Inspectorate through the application of clustering methods and recommendation systems. Clustering methods, specifically K-Means, are used to group supervision areas based on scores from various indicators, while the recommendation system provides specific improvement suggestions for each group. The results show that financial and performance supervision have high scores, with recommendations to maintain and improve existing standards, focusing on efficiency, transparency, and achieving performance targets. Compliance supervision shows variation in clustering, with recommendations for better enforcement of rules and regulations. Management supervision requires improving management and leadership effectiveness to enhance operational efficiency. Human resources supervision shows the best results, with a focus on improving employee competencies through training and development. The implementation of this technology is expected to optimize resource usage, enhance supervision effectiveness, and reduce operational costs. Clustering methods and recommendation systems provide deep insights into the condition of supervision and help the Inspectorate make more informed and strategic decisions. This study contributes to strengthening the supervision system and improving the overall performance of the Central Kalimantan Inspectorate, creating a more responsive, transparent, and accountable supervision system.

Recommendation Systems in E-Commerce Applications

Nowadays, there are more and more web applications of all kinds. Each of them solves a specific problem and makes life easier for its users. Web applications come in many different types: from a platform for learning courses and watching movies to an online store selling goods. The best systems are those that make things as easy as possible for the user, behave like old friends who know the behavior and tastes of their users and can predict their next move. It would be useful to integrate such system behavior into an online store system, as nowadays, a huge number of people prefer to buy goods online, saving time and effort. Thus, recommender systems have become an important tool for improving the efficiency of e-commerce stores and ensuring customer satisfaction. This study analyzes the main approaches to the application of recommender systems for online stores, substantiates the advantages and feasibility of the selected technologies for the implementation of an online store information system using neural networks.

Improved theoretical guarantee for rank aggregation via spectral method

Abstract Given pairwise comparisons between multiple items, how to rank them so that the ranking matches the observations? This problem, known as rank aggregation, has found many applications in sports, recommendation systems and other web applications. We focus on the ranking problem under the Erdös–Rényi outliers model: only a subset of pairwise comparisons is observed, being either clean or corrupted copies of the true score differences. We investigate the spectral ranking algorithms that are based on unnormalized and normalized data matrices. The key is to understand their performance in recovering the underlying scores of each item from the observed data. This reduces to deriving an entry-wise perturbation error bound between the top eigenvectors of the unnormalized/normalized data matrix and its population counterpart. By using the leave-one-out technique, we provide a sharper $\ell _{\infty }$-norm perturbation bound of the eigenvectors and derive an error bound on the maximum displacement for each item, with only $O(n\log n)$ samples. In addition, we also derive the sample complexity to perform top-$K$ ranking under mild assumptions. Our theoretical analysis improves upon the state-of-the-art results in terms of sample complexity, and our numerical experiments confirm these theoretical findings.

Gated graph spiking neural P network for session-based recommendation

Session-based recommendation (SBR) is a recommendation system application scenario that focuses on user interests and behaviors in sessions formed within a short period. However, existing SBR methods do not fully extract user contextual information, and gated graph neural networks(GNNs) still face challenges in handling long-term dependencies. The nonlinear spiking neural P system is a novel spiking neural-like computing model in which the nonlinear spiking mechanism is a typical feature. Based on this nonlinear spiking mechanism, we propose a new recurrent-like model called the extended gated spiking neural P model or the EGSNP model, which can effectively capture temporal relationships in their sequence and obtain the user’s contextual information to alleviate information extraction problems in SBR. Based on this EGSNP model, we propose a new type of GNN called the extended gated graph spiking neural P network or termed the EGGSNP network. Finally, we use the EGSNP model and EGGSNP network to construct a new SBR model for the session recommendation task, termed the SR-EGGSNP model. This SR-EGGSNP model can effectively extract contextual information and flexibly capture the long-term evolution of interest while alleviating the long-distance dependency problem in GNNs. We conducted extensive comparative experiments on two public datasets, and the results demonstrated the effectiveness of the proposed model and accuracy of the recommendations.

Classifications, evaluation metrics, datasets, and domains in recommendation services: A survey

Recommendation systems (RS) play a crucial role in assisting individuals in making suitable selections from an extensive array of products or services. This significantly mitigates the predicament of being overwhelmed by excessive information. RS finds powerful utility in online industries by vending products over the internet or furnishing online services. Given the potential for business expansion through their implementation, RS is relevant in such domains. This comprehensive review article overviews RS and its diverse variations and extensions. Specifically, this review provides a thorough comparative analysis for each method that encompasses many techniques employed in RS, encompassing content-based filtering, collaborative filtering, hybrid, and miscellaneous approaches. Notably, the article delves into the manifold applications of RS across various practical domains. Additionally, the assortment of evaluation metrics utilized across RS is explored. Finally, we conclude by encapsulating the distinct challenges RS encounters, which enhance their precision and dependability.

Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD

The application of recommendation systems has been applied in various types of platforms, especially applications for watching movies such as Netflix and Disney+. The recommendation system is purposed to make it easier for users, especially in choosing a movie because currently the number of movie productions is increasing every day. This research proposed a CBF movie recommendation system by comparing the performance of several semantic methods to be able to get the best rating prediction results. In order to improve the performance quality to get the best rating prediction results, this research utilized semantic feature methods by comparing the performance of the evaluation results produced by the TF-IDF method and word embedding applications, such as BERT, GPT-2, RoBERTa, and implemented RNN model to classify the results of rating prediction. The data were used to generate the recommendation system by involving 854 data movie and 39 accounts with a total of 34,056 movie reviews on Twitter. This research has succeeded in getting a method that produced rating predictions, namely RoBERTa. In the classification process with the RNN model and SGD optimization, the measurement results with confusion matrix by classifying the RoBERTA rating prediction obtained an evaluation value of 0.6514 loss, 95.59% accuracy, and 0.6514 precision.

Video Summarization using Object Detection Method

Video summarization is a fundamental challenge in the field of computer vision and multimedia processing, aimed at condensing lengthy videos into concise representations without compromising the essential content and context. This project focuses on the integration of object detection techniques into the process of video summarization, harnessing the power of deep learning to automatically identify and extract key objects and events from video sequences. By leveraging state-of-the-art object detection models and innovative summarization algorithms, this project aims to enhance the efficiency and effectiveness of video summarization, enabling users to quickly grasp the content and significance of videos without the need for exhaustive playback. The proposed approach not only streamlines video browsing and content comprehension but also holds potential applications in various domains, including surveillance, video indexing, and content recommendation systems

Enhancing Workplace Safety through Personalized Environmental Risk Assessment: An AI-Driven Approach in Industry 5.0

This paper describes an integrated monitoring system designed for individualized environmental risk assessment and management in the workplace. The system incorporates monitoring devices that measure dust, noise, ultraviolet radiation, illuminance, temperature, humidity, and flammable gases. Comprising monitoring devices, a server-based web application for employers, and a mobile application for workers, the system integrates the registration of workers’ health histories, such as common diseases and symptoms related to the monitored agents, and a web-based recommendation system. The recommendation system application uses classifiers to decide the risk/no risk per sensor and crosses this information with fixed rules to define recommendations. The system generates actionable alerts for companies to improve decision-making regarding professional activities and long-term safety planning by analyzing health information through fixed rules and exposure data through machine learning algorithms. As the system must handle sensitive data, data privacy is addressed in communication and data storage. The study provides test results that evaluate the performance of different machine learning models in building an effective recommendation system. Since it was not possible to find public datasets with all the sensor data needed to train artificial intelligence models, it was necessary to build a data generator for this work. By proposing an approach that focuses on individualized environmental risk assessment and management, considering workers’ health histories, this work is expected to contribute to enhancing occupational safety through computational technologies in the Industry 5.0 approach.

Application of recommendation system based on fuzzy logic in music culture communication and music teaching management

Music has been utilized throughout history as a medium for cultural communication and artistic expression, embodying various nations’ and societies’ ideologies and experiences. Music culture communication is crucial for encouraging cultural diversity and understanding and developing social cohesion and community building among people. Music teaching management is the process of setting up, arranging, and executing music education programs in a manner that successfully teaches students the essential skills and information necessary for becoming proficient musicians. Users’ exact preferences for various areas of attraction cannot be determined, nor are users’ choices for traditional music recommendations sufficiently accurate. A recommender system estimates or anticipates people’s preferences and offers appropriate recommendations. First, the sparsity problem emerges when insufficient data is accessible for the recommendation, and the coverage is one of the key drawbacks of social labeling. Cold start issues might be difficult since new music learners might not have given sufficient details about their musical tastes. Hence, the Hybridized Fuzzy logic-based Content and Collaborative Music Recommendation (HFC2MR) system is proposed to create personalized music teaching plans that are effective and engaging for each student based on their music preferences and learning outcomes. Enhanced Fuzzy C-Means clustering is used in collaborative recommendations to group users based on their shared musical tastes and to provide each user with more individualized, accurate music recommendations based on other users’ listening habits and preferences in the same cluster. Subsequently, an assessment of the recommender system using parameters like accuracy, precision, f1-score, and recall ratio is shown with optimal cluster selection. The coverage ratio is used to compare experimental data based on skill capacity covered through the assessment of music teaching. RMSE metric is used to evaluate the accuracy of students’ performance based on music attributes related to teaching goals.

Enhancing UCB-tuned and Asymptotically Optimal UCB Algorithms through Weighted Average Techniques in Multi-Armed Bandit Scenarios

This paper delves into the complexities of the Multi-Armed Bandit (MAB) problem, a fundamental concept in reinforcement learning and probability theory, with a focus on its application in recommendation systems and dynamic fields such as dynamic pricing and investment. It begins by shedding light on the essential paradox at the heart of the MAB problem – the balance between exploration and exploitation within limited parameters. The study primarily centers on Upper Confidence Bound (UCB) policies, especially UCB-tuned and Asymptotically Optimal UCB, noted for their adept balance between exploration and utilization. The novel contribution of this research is the enhancement of these UCB policies via an innovative weighted average method, leading to the development of WA-UCB-tuned and WA Asymptotically Optimal UCB algorithms. The research rigorously compares these optimized iterations with traditional UCB1, UCB-tuned, and Asymptotically Optimal UCB across varied MAB models featuring different numbers of arms. This study provides an exhaustive introduction to the MAB problem and pertinent UCB policies, the methodology behind the weighted average optimization, extensive experimental analysis, and comprehensive evaluations of the findings. The results showcase marked improvements in algorithmic performance, suggesting significant advancements in the domain of recommendation systems and other applications of the MAB problem.

A Analytical and Practical Insights into Multi-Armed Bandit Problems in Recommendation Systems

This paper delves into the application of the Multi-Armed Bandit (MAB) algorithm in recommendation systems, a tool increasingly prevalent across diverse sectors such as e-commerce, social networks, and news platforms. The primary objective of these systems is to curate content that resonates with user preferences, thereby enhancing user engagement and augmenting business revenue. Central to the optimization of these recommendation strategies is the careful balance between exploration - the pursuit of new, potentially relevant options - and exploitation - the utilization of known, popular choices. The MAB algorithm, an online learning method, adeptly navigates this balance. This study presents a detailed exploration of the MAB algorithm's theoretical underpinnings and its practical applications in recommendation systems. We implement these concepts using real-world datasets to assess their efficacy in such systems. The paper concludes by examining the benefits and constraints of employing MAB algorithms in recommendation contexts and proposes avenues for future research. This analysis aims to contribute to the ongoing evolution of recommendation systems, underscoring the pivotal role of MAB algorithms in their advancement.

Paper Recommendation Method based on Attention Mechanism and Graph Neural Network

At present, most recommendation technologies only consider text or citation information, which suffers from data sparseness and cold start problems. Therefore, an academic paper recommendation method based on attention mechanism and heterogeneous graph CAH is proposed. This method considers textual information and heterogeneous graph structure information to obtain a richer and more complete feature representation. Finally, cosine similarity is calculated to generate recommendations. The results show that compared with the content-based recommendation method, the accuracy rate, recall rate and f value of CAH method are increased by nearly 5.6%, 5.8% and 8.7%, respectively, which are significantly improved compared with the basic method. This method is expected to promote the in-depth application of recommendation systems in the field of artificial intelligence.

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Smart cities represent the convergence of information and communication technologies (ICT) with urban management to improve the quality of life of city dwellers. In this context, recommender systems, tools that offer personalised suggestions to city dwellers, have emerged as key contributors to this convergence. Their successful application in various areas of city life and their ability to process massive amounts of data generated in urban environments has expedited their status as a crucial technology in the evolution of city planning. Our methodology included reviewing the Web of Science database, resulting in 130 articles that, filtered for relevancy, were reduced to 86. The first stage consisted of carrying out a bibliometric analysis with the objective of analysing structural aspects with the SciMAT tool. Secondly, a systematic literature review was undertaken using the PRISMA 2020 statement. The results illustrated the different processes by which recommendations are filtered in areas such as tourism, health, mobility, and transport. This research is seen as a significant breakthrough that can drive the evolution and efficiency of smart cities, establishing a solid framework for future research in this dynamic field.

Image Captioning in News Report Scenario

Image captioning[1] strives to generate pertinent captions for specified images[56], situating itself at the crossroads of Computer Vision (CV) [2] and Natural Language Processing (NLP)[54]. This endeavor is of paramount importance with far-reaching applications in recommendation systems, news outlets, social media, and beyond. Particularly within the realm of news reporting, captions are expected to encompass detailed information, such as the identities of celebrities captured in the images. However, much of the existing body of work primarily centers around understanding scenes and actions.[3] In this paper, we explore the realm of image captioning specifically tailored for celebrity photographs, illustrating its broad potential for enhancing news industry practices. This exploration aims to augment automated news content generation, thereby facilitating a more nuanced dissemination of information.[57] Our endeavor shows a broader horizon, enriching the narrative in news reporting through a more intuitive image captioning framework.

Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning

Multi-domain learning (MDL) aims to train a model with minimal average risk across multiple overlapping but non-identical domains. To tackle the challenges of dataset bias and domain domination, numerous MDL approaches have been proposed from the perspectives of seeking commonalities by aligning distributions to reduce domain gap or reserving differences by implementing domain-specific towers, gates, and even experts. MDL models are becoming more and more complex with sophisticated network architectures or loss functions, introducing extra parameters and enlarging computation costs. In this paper, we propose a frustratingly easy and hyperparameter-free multi-domain learning method named Decoupled Training (D-Train). D-Train is a tri-phase general-to-specific training strategy that first pre-trains on all domains to warm up a root model, then post-trains on each domain by splitting into multi-heads, and finally fine-tunes the heads by fixing the backbone, enabling decouple training to achieve domain independence. Despite its extraordinary simplicity and efficiency, D-Train performs remarkably well in extensive evaluations of various datasets from standard benchmarks to applications of satellite imagery and recommender systems.