Design Of Recommendation System Research Articles

Human-centered design of a health recommender system for orthopaedic shoulder treatment

BackgroundRich data on diverse patients and their treatments and outcomes within Electronic Health Record (EHR) systems can be used to generate real world evidence. A health recommender system (HRS) framework can be applied to a decision support system application to generate data summaries for similar patients during the clinical encounter to assist physicians and patients in making evidence-based shared treatment decisions.ObjectiveA human-centered design (HCD) process was used to develop a HRS for treatment decision support in orthopaedic medicine, the Informatics Consult for Individualized Treatment (I-C-IT). We also evaluate the usability and utility of the system from the physician’s perspective, focusing on elements of utility and shared decision-making in orthopaedic medicine.MethodsThe HCD process for I-C-IT included 6 steps across three phases of analysis, design, and evaluation. A team of health informatics and comparative effectiveness researchers directly engaged with orthopaedic surgeon subject matter experts in a collaborative I-C-IT prototype design process. Ten orthopaedic surgeons participated in a mixed methods evaluation of the I-C-IT prototype that was produced.ResultsThe HCD process resulted in a prototype system, I-C-IT, with 14 data visualization elements and a set of design principles crucial for HRS for decision support. The overall standard system usability scale (SUS) score for the I-C-IT Webapp prototype was 88.75 indicating high usability. In addition, utility questions addressing shared decision-making found that 90% of orthopaedic surgeon respondents either strongly agreed or agreed that I-C-IT would help them make data informed decisions with their patients.ConclusionThe HCD process produced an HRS prototype that is capable of supporting orthopaedic surgeons and patients in their information needs during clinical encounters. Future research should focus on refining I-C-IT by incorporating patient feedback in future iterative cycles of system design and evaluation.

Design of Hybrid SVM Job recommender (HSJR) system for the overlapping target classes

Design of Hybrid SVM Job recommender (HSJR) system for the overlapping target classes

Towards Automated Model Design on Recommender Systems

The increasing popularity of deep learning models has created new opportunities for developing AI-based recommender systems. Designing recommender systems using deep neural networks requires careful architecture design, and further optimization demands extensive co-design efforts on jointly optimizing model architecture and hardware. Design automation, such as Automated Machine Learning (AutoML), is necessary to fully exploit the potential of recommender model design, including model choices and model-hardware co-design strategies. We introduce a novel paradigm that utilizes weight sharing to explore abundant solution spaces. Our paradigm creates a large supernet to search for optimal architectures and co-design strategies to address the challenges of data multi-modality and heterogeneity in the recommendation domain. From a model perspective, the supernet includes a variety of operators, dense connectivity, and dimension search options. From a co-design perspective, it encompasses versatile Processing-In-Memory (PIM) configurations to produce hardware-efficient models. Our solution space’s scale, heterogeneity, and complexity pose several challenges, which we address by proposing various techniques for training and evaluating the supernet. Our crafted models show promising results on three Click-Through Rates (CTR) prediction benchmarks, outperforming both manually designed and AutoML-crafted models with state-of-the-art performance when focusing solely on architecture search. From a co-design perspective, we achieve 2 × FLOPs efficiency, 1.8 × energy efficiency, and 1.5 × performance improvements in recommender models.

Design of news sentiment classification and recommendation system based on multi-model fusion and text similarity

To improve the performance of the news recommendation system, the study employs a multi-model fusion strategy in conjunction with three classification methods to improve the accuracy of sentiment classification. In addition, a text similarity-based recommendation algorithm is proposed to provide personalized recommendations to users. This is achieved by analyzing their interest features and key text features. In addition, a comprehensive recommendation system including news sentiment classification, recommendation algorithms, and a user interface is developed. The study showed that the sentiment classification method based on multi-model fusion had an accuracy rate of 0.95, which was an improvement of 0.10 compared to the support vector machine. Furthermore, the recommendation algorithm based on text similarity calculation demonstrated an accuracy, recall, and F1 value of 0.806, 0.804, and 0.806, respectively, which outperformed the collaborative filtering algorithm and the matrix decomposition recommendation algorithm. The average satisfaction rating for the news recommendation content was above 90 points, indicating that it met user needs. The findings indicate that consumers perceive the personalized news suggestion system to be well-received and that it functions effectively in terms of reaction time and resource consumption. This research offers an effective solution for news recommendation in the era of information overload, which holds significant theoretical and practical value.

A small neighborhood fabric recommender system based on user historical behavior and preference

In the current landscape, textile companies need rapid, precise, and personalized assistance, especially as digitalization and information, such as e-commerce and websites, are developing rapidly. Textile enterprises can mine user preferences to make personalized recommendations of appropriate textile fabrics; this is not only in keeping with current trends, but more importantly, can control production costs and improve user satisfaction. To support the transformation of textile enterprises to a small-batch and multi-variety business model, this paper proposes a fabric recommendation system; specifically, a fabric recommender system based on user historical behavior and preference is proposed. The proposed method is mainly based on the integration of preference, user activity, and rating; firstly, according to the fabric products purchased by the user, the features of the fabric are mined using a neural network to obtain the user’s preferences; secondly, neighbors with similar user behaviors are found according to similarity in users’ activity; and finally, understanding of the fabric is enhanced through a matrix based on the fabric ratings of the user. A focus on recommendation algorithm parameterization, including selection, thresholding, and neighborhood optimization, elevates the recommendation quality. A user–fabric dataset was used for experimental verification, and included the user’s purchase score of the fabric and the image of the fabric. Comparative analyses demonstrate superior precision with fewer neighborhoods, achieving a score of up to 0.93. Our research provides insights into user behavior and personalization, guiding future recommender system design and optimization.

Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblank

Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design.

Optimal design of neural network-based fuzzy predictive control model for recommending educational resources in the context of information technology

Abstract As the information technology develops, educational resource recommendation system has become an indispensable part of the education field. At the same time, people’s demand for personalised educational resources is also growing; therefore, how to accurately predict user demand and provide personalised recommendations has become an important issue. In this study, a fuzzy predictive control model on the grounds of neural network is proposed to optimise the design of educational resource recommendation in the context of information technology. After experimental testing, the model recommendation fit reached 95.16 and 92.91% on the two test datasets, which are significantly higher than the control model. The average F1 values of the proposed model also reached 95.21 and 88.77%, which are higher than the control model. In other control experiments, the proposed model of this study also has a better performance. The relevant outcomes showcase that the predictive performance and recommendation effect of the model can be further enhanced by improving the structure of the neural network and the parameter optimisation method. Meanwhile, the proposed model has high performance in information overload and personalised demand, which offers a useful reference for the optimal design of educational resource recommendation system.

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 of adaptive recommendation system for autism children using optimal feature selection-based adaptive dilated 1DCNN-LSTM with attention mechanism

One kind of neurological disorder is caused in the brain which is defined as Autism Spectrum Disorder (ASD). It has acquired the symptoms that appear in young children. In addition to that, it influences how the individual behaves and learns as well as communicates and interacts with others. More specifically, the term Autism is defined as a developmental disorder that impacts communication and social skills and it may vary from mental handicap cases to relieving superior cognitive abilities, intact, and the characteristic pattern of poor. Moreover, the school activities have acquired various difficulties to the given model that include changes in expected routines, intense sensory stimulation, noisy or disordered environments, and social interactions. Consequently, the conventional approaches face certain limitations like user privacy, scalability, and cold-start. Here, a novel suggestion system for autistic children is developed to detect distractions and anxious situations using deep learning and then treat the children based on their abilities. It has helped to prevent the risk to children. The data is given to the selection of the feature stage. The weight optimization is performed using the Modified Garter Snake Optimization Algorithm (MGSOA) during the selection of features. Then, the selected features are given to the Adaptive Dilated One Dimensional Conventional Neural Network (1DCNN) and Long Short-Term Memory (LSTM) with Attention Mechanism termed AD-1DCNN + LSTM-AMfor detecting the autism disorder for children. Here, the parameter optimization is performed using MGSOA optimization. It effectively forecasts the symptoms in a short time. This optimization helps to provide reliable and flexible outcomes for the developed recommendation system for autistic children. The developed recommendation system for autistic children is compared to baseline techniques with efficacy metrics to visualize elevated results.

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

In recent years, we have seen a significant proliferation of e-learning platforms. E-learning platforms allow teachers to create digital courses in a more effective and time-saving way, but several flaws hinder their actual success. One main problem is that teachers have difficulties finding and combining open-access learning materials that match their specific needs precisely when there are so many to choose from. This paper proposes a new strategy for creating digital courses that use learning objects (LOs) as primary elements. The idea consists of using an intelligent chatbot to assist teachers in their activities. Defined using RASA technology, the chatbot asks for information about the course the teacher has to create based on her/his profile and needs. It suggests the best LOs and how to combine them according to their prerequisites and outcomes. A chatbot-based recommendation system provides suggestions through BERT, a machine-learning model based on Transformers, to define the semantic similarity between the entered data and the LOs metadata. In addition, the chatbot also suggests how to combine the LOs into a final learning path. Finally, the paper presents some preliminary results about tests carried out by teachers in creating their digital courses.

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.