Design Of Recommendation System 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.

Extracting Political Interest Model from Interaction Data Based on Novel Word-level Bias Assignment

In democratic countries, political interest is deeply involved in people's daily lives. Research in political consumerism shows that product purchase decision is also influenced by the political orientation of the consumer. In traditional recommendation system design, user interest in an item is provided by a unified model. Recently, interest disentanglement methods have been introduced. It is shown that by disentangling interest factors such as conformity and private interest, recommendation performance can be significantly improved. However, few studies attempt to disentangle political interest in purchase behavior, which is bipolar. In this paper, we propose a method to extract political interest model from e-commerce interaction data, which is supported by a novel word-level political bias assignment. For the bias assignment part, we improved a political bias distilling method. For the political interest model extraction part, we extend a one-side bias method to make it support bipolar bias. We compare our method with state-of-the-art baseline methods in several evaluation settings, and the experimental results show that our method can achieve superior performance. Further investigation shows that our method is consistent with theories of political consumerism.

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.

SRRS: Design and Development of a Scholarly Reciprocal Recommendation System

SRRS: Design and Development of a Scholarly Reciprocal Recommendation System

Analysis on recommendation systems based on ML and DL approaches

The explosive growth of the Internet has led to an unprecedented increase in available information, creating a major challenge in identifying content that aligns with user interests. Recommendation systems (RS) address this challenge by providing personalized content recommendations based on user behavior and preferences. This article systematically reviews the evolution of recommendation systems with a focus on the methodologies of machine learning (ML) and deep learning (DL). It highlights core technologies in ML-based RS, such as content-based recommendation, collaborative filtering (CF), and hybrid filtering (HF). The article evaluates the effectiveness of various DL models, including autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN). The review also addresses key challenges faced by RS, such as cold start issues and the need for improved model transparency and interpretability. DL methods are shown to significantly enhance recommendation accuracy by leveraging complex patterns in large-scale data, to improve the user experience. Future research directions include refining data preprocessing, enhancing feature engineering, compressing and accelerating DL models, and improving interpretability through advanced mechanisms. This study provides valuable insights and references for researchers aiming to advance recommendation system design and performance optimization.

Design and Evaluation of a Learning Resource Recommendation System Based on Machine Learning

In the era of information overload, learners are often overwhelmed by the vast amount of educational content available online. To address this challenge, we have developed a Learning Resource Recommendation System (LRRS) that leverages machine learning techniques to provide personalized learning material suggestions. The LRRS is designed to analyze learners' profiles, preferences, and learning behaviors to deliver a tailored learning experience. By employing collaborative filtering, content-based filtering, and hybrid methods, the system can predict and recommend resources that are most relevant to individual learners. This paper presents the architecture of the LRRS, the methodologies used for recommendation, and the results of its evaluation. We conducted experiments using a dataset of learners and resources to assess the accuracy and effectiveness of the system. The evaluation metrics, including precision, recall, and F1-score, demonstrate the system's ability to enhance the learning process by providing relevant and engaging content. The LRRS also includes a feedback mechanism that allows for continuous improvement of the recommendation algorithms based on user interactions. The findings of this study contribute to the field of educational technology by offering insights into the development of intelligent systems that can support personalized learning.

A graph neural network-based teammate recommendation model for knowledge-intensive crowdsourcing

Current research on teammate recommendation decision in various fields is difficult to apply to knowledge-intensive crowdsourcing, which needs to make a trade-off between solver willingness and team performance. To weigh the bilateral benefits between the solver and the seeker, this paper proposes a teammate recommendation model based on multi-criteria decision-making of solver willingness and team performance. The prediction of willingness to team up is driven by explicit competitiveness, teammate familiarity, and social capital. By adapting the graph-based deep learning model, we transform the solver performance prediction into a bipartite network link prediction problem. We jointly compute the solver willingness and team performance to make the final recommendation decisions. The experimental results based on Kaggle data prove the effectiveness of our method. The findings provide managerial implications and a practical reference for the design and operation of teammate recommender systems.

Generation of Graphic Design Color Schemes Based on CMYK Color Model and Corrosion Algorithms

Graphic design colour schemes play a fundamental role in shaping the visual identity and impact of a design. This paper proposed an efficient Feature Extraction Madhami Fuzzy Clustering Probabilistic Corrosion (FEMFcPC) for the graphic design. The proposed FEMFcPC model comprises the CMYK color scheme for the estimation of the features in the images. With the estimation of the FEMFcPC model corrosion algorithm is implemented for the computation of the graphic design features. Once the features are estimated the related features for the graphic design are designed with the mamdhami fuzzy clustering model. The estimated features are clustered with the CMYK color schemes. The proposed FEMFcPC model estimates the patterns and relationships among graphic designs to provide valuable insights for design categorization and recommendation systems. A dataset comprising various graphic design examples with their corresponding CMYK color schemes is utilized for analysis. Through the application of the FEMFcPC algorithm, clustering results are obtained, revealing distinct grouping patterns and similarities among the designs. The simulation results stated that the proposed FEMFcPC model achieves a significant color scheme for the graphic design. Through the clustering process, the proposed FEMFcPC model with the values of the probabilistic features for the graphic design. The Key findings include the identification of dominant clusters, even distribution of designs across multiple clusters, detection of outliers, and assessment of cluster density. These insights offer significant implications for design categorization, recommendation systems, and targeted marketing strategies within the graphic design domain. The study contributes to advancing our understanding of graphic design analysis and provides a foundation for future research in this field.

How Can Recommender Systems Benefit from Large Language Models: A Survey

With the rapid development of online services and web applications, recommender systems (RS) have become increasingly indispensable for mitigating information overload and matching users’ information needs by providing personalized suggestions over items. Although the RS research community has made remarkable progress over the past decades, conventional recommendation models (CRM) still have some limitations, e.g. , lacking open-domain world knowledge, and difficulties in comprehending users’ underlying preferences and motivations. Meanwhile, large language models (LLM) have shown impressive general intelligence and human-like capabilities for various natural language processing (NLP) tasks, which mainly stem from their extensive open-world knowledge, logical and commonsense reasoning abilities, as well as their comprehension of human culture and society. Consequently, the emergence of LLM is inspiring the design of recommender systems and pointing out a promising research direction, i.e. , whether we can incorporate LLM and benefit from their common knowledge and capabilities to compensate for the limitations of CRM. In this paper, we conduct a comprehensive survey on this research direction, and draw a bird’s-eye view from the perspective of the whole pipeline in real-world recommender systems. Specifically, we summarize existing research works from two orthogonal aspects: where and how to adapt LLM to RS. For the “ WHERE ” question, we discuss the roles that LLM could play in different stages of the recommendation pipeline, i.e. , feature engineering, feature encoder, scoring/ranking function, user interaction, and pipeline controller. For the “ HOW ” question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria, i.e. , whether to tune LLM or not during training, and whether to involve conventional recommendation models for inference. Detailed analysis and general development paths are provided for both “WHERE” and “HOW” questions, respectively. Then, we highlight the key challenges in adapting LLM to RS from three aspects, i.e. , efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects. To further facilitate the research community of LLM-enhanced recommender systems, we actively maintain a GitHub repository for papers and other related resources in this rising direction 1 .

Design and Development of an Efficient Demographic-based Movie Recommender System using Hybrid Machine Learning Techniques

Movie Recommender systems are frequently used in academics and industry to give users with relevant, engaging material based on their rating history. However, most traditional methods suffer from the cold-start problem, which is the initial lack of item ratings and data sparsity. The Hybrid Machine Learning (ML) technique is proposed for a movie recommendation system. Demographic data is collected from the Movie Lens dataset, and attributes are evaluated using the Attribute Analysis module. The Aquila Optimization Algorithm is used to select the best attributes, while Random Forest classifier is used for classification. Data is clustered using Fuzzy Probabilistic Cmeans Clustering Algorithm (FPCCA), and the Correspondence Index Assessment Phase (CIAP) uses Bhattacharyya Coefficient in Collaborative Filtering (BCCF) for similarity index calculation. The Outcomes gives the proposed method obtained low error, such as MAE has 0.44, RMSE has 0.63 compared with the baseline methods.

Design of a conversational recommender system in education

Design of a conversational recommender system in education

Who is recommended matters: an investigation from a relational perspective

ABSTRACT Since the performance of the platform can be largely influenced by the relationships between users, it is important to consider the effect of recommender systems from a relational perspective, which is overlooked in the literature. Our study complements the literature by distinguishing recommended candidates as either familiar or unfamiliar based on relationship embeddedness. Using a recommender system’s launch on a freight exchange platform as a natural experiment, we employ difference-in-differences estimations to quantify the effects of recommending diverse candidates on user’s acceptance and overall transaction frequency. Results show that recommendations with familiar candidates are more likely to be accepted, but lead to a lower user’s overall transaction frequency. On the contrary, recommendations with unfamiliar candidates result in a higher user’s overall transaction frequency, suggesting that the positive effect of the recommender system is primarily driven by recommendations with unfamiliar candidates. We attribute these findings to informative signals of more matching opportunities conveyed by recommendations with unfamiliar candidates, which elevate user’s evaluation of the platform and stimulate more transactions. Our work contributes to the literature by highlighting a relational perspective in the design of recommender systems and the importance of diversity rather than accuracy.

Information Transparency With Targeting Technology for Online Service Operations Platform

Social technologies have enabled the emergence of online platforms that provide offline service consultations and recommendations. In this environment, economic inefficiency arises when customers are not fully aware of their horizontally differentiated preferences. With its expertise or data dominance, a platform can be more informed about customers’ hidden preferences. We focus on an instrumental social technology, that is, targeting, which is a type of data-driven personalized information provision to manipulate customers’ beliefs about service quality. We propose a Hotelling model wherein customers are sensitive to the delays for service while making Bayesian belief updates based on a platform’s recommendations. When customers self-select their favorite service, their choices impose negative externalities through congestion and welfare loss. Our results indicate that service recommendations allow customers to navigate toward the more appropriate service, thus improving matching efficiency, reducing congestion costs, and enhancing aggregate customer welfare. We further identify the role of “information transparency” and study how the platform should strategically release information by making personalized service recommendations to customers. Interestingly, when a customer-centric platform maximizes aggregate customer welfare, we identify the “value of opaqueness” by strategically withholding service recommendations from a subset of customers and notice that this effect is more pronounced for a profit-seeking platform. Our results offer a better understanding of information transparency policies in the joint design of service recommendation systems and pricing mechanisms.

Anonymity‐Aware Framework for Designing Recommender Systems

Due to increasing secondary use of data, recommender systems using anonymized data are in demand. However, implementing a recommender system requires complicated data processing and programming, and the relationship between anonymization level and recommendation quality has not been investigated. Therefore, this study proposes a framework that facilitates the development of recommender systems. Additionally, a method is proposed for quantitatively evaluating recommendation quality when the anonymization level is varied. The proposed method promotes data utilization by recommender systems and determination of compensation for providing data based on anonymization level. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Deep learning-based personalized learning recommendation system design for "T++" Guzheng Pedagogy

Deep learning-based personalized learning recommendation system design for "T++" Guzheng Pedagogy

Client-centered detached modular housing: natural language processing-enabled design recommender system

Abstract Designing modular housing is a complex task that necessitates a thorough understanding of the diverse needs of clients in terms of both design aesthetics and floor plan layout. Furthermore, adhering to design for manufacture and assembly (DfMA) principles adds to the complexity, as these are essential modular home requirements. Traditional construction methods frequently fail to meet the specific needs of both clients and DfMA, potentially resulting in suboptimal design solutions. Incorporating client requirements during the design phase necessitates the use of an effective system and framework to reduce changes in subsequent project stages. Existing literature lacks a suitable approach, particularly in the context of modular housing. To address this gap, this paper introduces an artificial intelligence–building information modeling recommender system (RS) for detached modular housing design. The system processes client requirements entered as text utilizing the Word2vec algorithm with the GloVe dataset, refined through transfer learning using surveyed client data of housing needs. The system recommends three distinct modular building design alternatives sourced from a building information modeling models database using cosine and Euclidean similarity functions. A sensitivity analysis ensures that client needs are considered fairly, increasing the robustness of the RS. By incorporating natural language processing, this system transforms the construction industry by making initial designs more client-centric compared with traditional methods. Furthermore, it promotes improved collaboration among clients, design, and construction teams, reducing modifications to design in later stages of construction.

Enhancing Educational Design Capabilities through Interactive Mobile and Adaptive Learning Platforms: An Empirical Study

With the continuous advancement of educational technology, interactive mobile and adaptive learning platforms are playing an increasingly important role in the field of education. This is particularly evident in the development of educational design skills among education students, as these platforms showcase their unique value. Educational design capability is a crucial skill for education students, directly related to the quality of designing and implementing future teaching activities. Traditional methods of education often fail to fully consider individual student differences, resulting in inadequate cultivation of personalized capabilities. This study aims to explore and achieve precise cultivation of educational design capabilities in education students through interactive, mobile, and adaptive learning platforms. This paper first reviews the current application of interactive mobile and adaptive learning platforms to cultivate educational design capabilities. It highlights deficiencies in existing research methods related to personalized matching and recommendation system design. To address these deficiencies, this study proposes a new set of adaptive matching methods. These methods include capability characteristic recognition based on competitive advantage thinking, the construction of individual strength models, as well as matching calculation, and decision- making scheme optimization using the projection decision method and the Hungarian method. Additionally, the study designs a learning project recommendation algorithm based on explicit ratings to enhance the accuracy and personalization of learning project recommendations. The application of these methods not only enhances the educational design capabilities of education students but also provides new theoretical support and practical guidance for the development of interactive, mobile, and adaptive learning platforms.

Design of an English vocabulary e-learning recommendation system based on word bag model and recurrent neural network algorithm

This article takes learning English vocabulary as the foundation, constructs precise classification strategies, and designs a recommendation system for English vocabulary learning. This article constructs a recommendation system for English vocabulary learning based on the word bag calculation model and recursive neural network calculation method, which has great significance, and also conducts in-depth research on this system. Thus, a classification calculation method based on the word bag calculation model was proposed. The calculation method in this paper is different from traditional calculation methods, and the classification accuracy in this paper is higher. Meanwhile, recurrent neural networks themselves have one or more feedback loops that can feed back information from the neural network to other neural networks, resulting in recurrent networks with different structures. After a series of research results, it has been found that the SIFT method takes longer to extract than the SURF method, and the number of extracted features is relatively large. Therefore, the recommendation system for English vocabulary learning proposed in this article can be improved according to the different needs of users, in order to better meet their individual requirements.

Entity Footprinting: Modeling Contextual User States via Digital Activity Monitoring

Our digital life consists of activities that are organized around tasks and exhibit different user states in the digital contexts around these activities. Previous works have shown that digital activity monitoring can be used to predict entities that users will need to perform digital tasks. There have been methods developed to automatically detect the tasks of a user. However, these studies typically support only specific applications and tasks, and relatively little research has been conducted on real-life digital activities. This article introduces user state modeling and prediction with contextual information captured as entities, recorded from real-world digital user behavior, called entity footprinting —a system that records users’ digital activities on their screens and proactively provides useful entities across application boundaries without requiring explicit query formulation. Our methodology is to detect contextual user states using latent representations of entities occurring in digital activities. Using topic models and recurrent neural networks, the model learns the latent representation of concurrent entities and their sequential relationships. We report a field study in which the digital activities of 13 people were recorded continuously for 14 days. The model learned from this data is used to (1) predict contextual user states and (2) predict relevant entities for the detected states. The results show improved user state detection accuracy and entity prediction performance compared to static, heuristic, and basic topic models. Our findings have implications for the design of proactive recommendation systems that can implicitly infer users’ contextual state by monitoring users’ digital activities and proactively recommending the right information at the right time.

Investigating the working principle of the recommending system and designing a personal music recommending system

This paper, Investigating the Working Principle of the Recommending System and Designing a Personal Music Recommending System, delves into the intricacies of recommendation systems, with a specific focus on music. It begins by exploring the fundamental principles that underpin recommendation systems.The latter part of the paper presents the design of a novel personal music recommendation system. This system leverages the results of K-means clustering to provide faster personalized music recommendations. It considers various auditory factors such as valence (the musical positiveness conveyed by a track), duration (the length of the track), and energy (a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity). These factors are calculated by Spotify and made available in an open data set. This research contributes significantly to the field by providing a deeper understanding of recommendation systems and proposing an innovative solution for personalized music recommendations. The novelty of this system lies in its use of K-means clustering and the specific auditory factors it considers, which sets it apart from existing systems.