Personalized Recommendation System Research Articles

ChatGPT based recommendation system for retail shops

The rapid growth of e-commerce platforms has emphasized the significance of personalized recommendation systems in enhancing user engagement and satisfaction. This research paper presents the development and evaluation of an innovative Product Recommendation System that leverages advanced Artificial Intelligence (AI) techniques to provide tailored product suggestions. The primary objective is to create a user-centric experience by integrating an AI assistant, enabling natural and interactive interactions. Through a comprehensive survey conducted to understand customer behaviour while purchasing product using AI, the study aims to assess the system's effectiveness in delivering accurate recommendations and providing a seamless purchasing experience. The paper contributes to the field by showcasing the practical implementation of AI-driven recommendation systems, highlighting their potential to transform e-commerce interactions.

Dynamic Personalized Recipe Recommendations Based on Facial Health Recognition

Nowadays, the demand for more personalized healthy food recipes is increasing over time, but traditional personalized recipe recommendation systems often tend to overlook the differences that exist in different users’ health conditions. This paper aims to address the issue by proposing an innovative personalized health recipe recommendation framework. This framework is based on different health needs, aiming to address users with different health conditions. More explicitly, the hybrid recommendation algorithm based on facial health recognition is designed to recommend the most suitable recipes for the user by taking into account the user’s health status and preferences. In addition, the fitness factor will adjust the recommendation results to best meet the user’s taste preferences and health goals. Experimental results and user research results show that the recommendation results of this framework are more accurate compared to existing systems, and therefore, users are more satisfied.

Maximizing user experience with LLMOps-driven personalized recommendation systems

The integration of LLMOps into personalized recommendation systems marks a significant advancement in managing LLM-driven applications. This innovation presents both opportunities and challenges for enterprises, requiring specialized teams to navigate the complexity of engineering technology while prioritizing data security and model interpretability. By leveraging LLMOps, enterprises can enhance the efficiency and reliability of large-scale machine learning models, driving personalized recommendations aligned with user preferences. Despite ethical considerations, LLMOps is poised for widespread adoption, promising more efficient and secure machine learning services that elevate user experience and shape the future of personalized recommendation systems.

Simulation of E-learning in English personalized learning recommendation system based on Markov chain algorithm and adaptive learning algorithm

The research topic of this article is the English personalized learning recommendation module based on Markov chain algorithm and adaptive learning algorithm. Personalized learning recommendation systems have been widely applied in the field of education. Firstly, this article uses Markov chain algorithm to analyze user learning behavior data, abstract user learning behavior as a process of state transition, predict their future behavior based on their past behavior, better understand their learning needs and interests, and provide more personalized learning recommendations for them. Subsequently, combined with adaptive learning algorithms, recommendation strategies and content are dynamically adjusted based on the user's learning goals and level, providing more accurate and effective learning recommendations according to their personalized needs and learning progress. By comparing with traditional recommendation systems, evaluate the accuracy and personalization of the recommendation module to better meet the learning needs of users.

Enhanced Freelance Matching: Integrated Data Analysis and Machine Learning Techniques

The objective of this research is to devise a personalized recommendation system for a freelancing platform to optimize the freelancer project matching process. This enhancement is intended to improve user experience and increase the success rate of projects. The system will recommend projects to freelancers based on their skills and preferences by employing data analysis and machine learning methodologies. The research methodology adheres to the Cross Industry Standard Process for Data Mining (CRISP-DM), incorporating six stages: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The proposed project employs a hybrid recommendation strategy, integrating Content-Based Filtering through KNearest Neighbors (K-NN) and Cosine Similarity, Collaborative Filtering via Singular Value Decomposition (SVD), and recommendations derived from Word2vec. Evaluation metrics such as precision, recall, F1 score, MAP, and MRR are utilized to assess model performance. The results, including precision scores of 0.80 for KNN and 0.728 for SVD, recall scores of 0.60 for KNN and 0.623 for SVD, and F1 scores of 0.69 for KNN and 0.671 for SVD, as well as a MAP of 0.75 and MRR of 0.80 for Word2vec, demonstrate the efficacy of the hybrid recommendation system in delivering accurate and varied project suggestions to freelancers, with a weighted average ensemble learning model emerging as the most effective solution.

A Comprehensive Web-Based Solution Using Django Framework and SQLite Database

In today's competitive job market, universities play a crucial role in preparing students for successful careers. A well-designed placement cell website serves as a vital bridge between students and potential employers, facilitating seamless communication and fostering employment opportunities. This paper presents the abstract of such a website, aimed at enhancing student employability. The placement cell website offers a user-friendly interface, providing students with easy access to a myriad of resources such as job listings, internship opportunities, and career guidance materials. Employers, on the other hand, can efficiently browse through student profiles and post job openings, creating a dynamic ecosystem for talent acquisition. The website incorporates tools for interview preparation, and skill assessment, empowering students to present themselves effectively in the job market. Furthermore, the website alerts students with relevant job opportunities based on their qualifications and preferences. This personalized recommendation system enhances the efficiency of the job search process, increasing the likelihood of successful placements. To foster collaboration between academia and industry, the website facilitates communication between students, alumni, faculty members, and potential employers through forums, webinars, and networking events. This collaborative environment promotes knowledge sharing, mentorship, and professional development, ultimately enriching the overall student experience.

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

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

An adaptable and personalized framework for top-N course recommendations in online learning

In recent years, the proliferation of Massive Open Online Courses (MOOC) platforms on a global scale has been remarkable. Learners can now meet their learning demands with the help of MOOC. However, learners might not understand the course material well if they have access to a lot of information due to their inadequate expertise and cognitive ability. Personalized Recommender Systems (RSs), a cutting-edge technology, can assist in addressing this issue. It greatly increases resource acquisition through personalized availability for various people of all ages. Intelligent learning methods, such as machine learning and Reinforcement Learning (RL) can be used in RS challenges. However, machine learning needs supervised data and classical RL is not suitable for multi-task recommendations in online learning platforms. To address these challenges, the proposed framework integrates a Deep Reinforcement Learning (DRL) and multi-agent approach. This adaptive system personalizes the learning experience by considering key factors such as learner sentiments, learning style, preferences, competency, and adaptive difficulty levels. We formulate the interactive RS problem using a DRL-based Actor-Critic model named DRR, treating recommendations as a sequential decision-making process. The DRR enables the system to provide top-N course recommendations and personalized learning paths, enriching the student's experience. Extensive experiments on a MOOC dataset such as the 100 K Coursera course review validate the proposed DRR model, demonstrating its superiority over baseline models in major evaluation metrics for long-term recommendations. The outcomes of this research contribute to the field of e-learning technology, guiding the design and implementation of course RSs, to facilitate personalized and relevant recommendations for online learning students.

Design of Personalized Recommendation System for College Education Based on Multivariate Hybrid Criteria Fuzzy Algorithm

The learner model's design is a most important aspect of a personalized college student education recommendation system. Currently, most learner models need more scientific focus, relying on a single method to collect dimensions and feature attributes with low computing costs. Hence, this paper introduced a Multivariate Hybrid Criteria Fuzzy Algorithm (MHCFA) for personalized college education recommendation. The MHCFA is trained by Social Feedback Artificial Tree (SFAT), where SFAT is the combination of Social Optimization Algorithm (SOA) and Feedback Artificial Tree (FAT). In addition, Deep Fuzzy Clustering (DFC) is utilized to group college education content. The RV-Coefficient is employed to select the best content. Moreover, the feature is extracted by All Caps and numerical for further personalized recommendations. In addition, the test results show that SFAT_MHCFA performs better in Precision, Recall, and F-Measure where the values gained 0.989, 0.878, and 0.859, respectively.

Personalized Recommendation of English Online Teaching Content Based on Logistic Regression Algorithm

English online teaching content encompasses a wide range of materials designed to facilitate language learning in digital environments. These resources include interactive lessons, video tutorials, audio recordings, quizzes, vocabulary exercises, grammar explanations, and authentic texts such as articles, short stories, and dialogues. The content aims to cover various language skills, including listening, speaking, reading, and writing, catering to learners of different proficiency levels and learning styles. This paper introduces a Cross-Domain Personalized Recommendation System (CDPRS) designed to enhance the delivery of English language teaching content. Leveraging machine learning algorithms and data analytics techniques, the CDPRS tailors recommendations to individual user preferences, learning objectives, and proficiency levels across diverse content domains. Through a series of analyses and evaluations, including regression analysis and simulation studies, the effectiveness and utility of the CDPRS are demonstrated. Through a series of analyses and evaluations, including regression analysis and simulation studies, the effectiveness and utility of the CDPRS are demonstrated. Results indicate that the system achieves high levels of accuracy (mean accuracy = 0.85), diversity (mean diversity = 0.72), novelty (mean novelty = 0.78), and user satisfaction (mean satisfaction = 0.82) in providing personalized recommendations.

An Exhaustive Analysis of Reinforcement Learning Implementations within Recommendation System Paradigms

This manuscript begins with a detailed exploration of reinforcement learning, presenting a thorough analysis of its core models and algorithms. It contrasts these with traditional machine learning approaches, providing a nuanced comparative study. Following this, the paper dives into the origins and development of recommendation systems. It methodically classifies and explains the unique features of various prevalent recommendation algorithms. The focus of this research is the innovative integration of reinforcement learning in recommendation systems. It carefully examines how reinforcement learning can be merged with these systems, highlighting current challenges and outlining potential directions for future research, supported by empirical examples from practical applications. This comprehensive approach offers a deep insight into the interplay between advanced machine learning techniques and their application in improving recommendation systems. The manuscript aims to contribute significantly to the field by not only elucidating the theoretical underpinnings of these technologies but also by demonstrating their real-world efficacy and potential. Through this, it seeks to pave the way for more sophisticated, efficient, and personalized recommendation systems, driven by the latest advancements in machine learning.

Effectiveness of personalized meal recommendation in improving dietary behaviors of Chinese community-dwelling elders: study protocol for a cluster randomized controlled trial

BackgroundInappropriate eating behaviors, particularly a lack of food diversity and poor diet quality, have a significant impact on the prognosis of certain chronic conditions and exacerbate these conditions in the community-dwelling elderly population. Current dietary interventions for the elderly have not adequately considered the nutritional needs associated with multiple chronic conditions and personal dietary preferences of elderly individuals. A personalized recommendation system has been recognized as a promising approach to address this gap. However, its effectiveness as a component of an elderly-targeted dietary intervention in real-world settings remains unknown. Additionally, it is unclear whether this intervention approach will be user-friendly for the elderly. Therefore, this study aims to examine the effectiveness of a personalized meal recommendation system designed to improve dietary behavior in community-dwelling elders. The implementation process in terms of System usability and satisfaction will also be assessed.MethodsThe trial has been designed as a 6-month, non-blinded, parallel two-arm trial. One hundred fifty community-dwelling elders who meet the eligibility criteria will be enrolled. Subjects will be allocated to either the intervention group, receiving personalized meal recommendations and access to corresponding food provided as one component of the intervention, as well as health education on elder nutrition topics, or the control group, which will receive nutritional health education lectures. Outcomes will be measured at three time points: baseline at 0 months, 3 months, and 6 months. The primary outcomes will include dietary diversity (DDS) and diet quality (CDGI-E) of enrolled community-dwelling elders, representing their dietary behavior improvement, along with dietary behavior adherence to recommended meals. Secondary outcomes will measure the perceived acceptability and usability of the personalized meal recommendation system for the intervention group. Exploratory outcomes will include changes in the nutritional status and anthropometric measurements of the community-dwelling elders.DiscussionThis study aims to examine the effectiveness, acceptability, and usability of a personalized meal recommendation system as a data-driven dietary intervention to benefit community-dwelling elders. The successful implementation will inform the future development and integration of digital health strategies in daily nutrition support for the elderly.Trial registrationChinese Clinical Trial Registry ChiCTR2300074912. Registered on August 20, 2023, https://www.chictr.org.cn/showproj.html?proj=127583

Fashion Recommendation System and Virtual Trial Room

The fashion industry has recently witnessed massive growth in the clothing and textile industries. In e-commerce marketplaces, where there are many options accessible, relevant product material or information can be filtered, arranged, and efficiently communicated to users. As the standard of living increased, people's attention began to shift towards fashion, which is considered to be a common form of aesthetic expression. The development of the fashion business over time has been influenced by this human tendency. The abundance of clothing alternatives on e-commerce sites, however, has made it more difficult for customers to select the ideal outfit. In this article, we describe a personalised fashion recommender system that generates suggestions for the user based on input. In order to enhance the consumer shopping experience, we developed a software application that combines a recommender system with the OpenCV and TensorFlow technologies. This will enable customers to virtually try on clothing without having to wait in a large queue. The application uses OpenCV to map the clothes onto the customer’s body, the customer can then have a look at it and decide whether to buy the clothes or not. According to the results, there is no need to visit trial rooms because the mapping is done precisely onto the user's body. Thus, our app offers a quick, simple, and accurate method of trying on clothing, and we think that will help shape the retail sector.

A Collaborative Filtering-Based Recommender Systems approach for Multifarious applications

Recommender systems are crucial in today's IT landscape, enhancing user experiences in various industries. Collaborative filtering (CF) is a key approach, using historical interaction data to predict user preferences. This paper presents CF advancements, with a focus on latent factor models that represent users and items in a compact feature space. It also addresses sparsity issues with techniques like neighborhood-based approaches and content augmentation. Contextual CF, which incorporates temporal and contextual dynamics, is explored through methods like matrix factorization with side information and session-based recommendation. Evaluation metrics such as MAE and RMSE, along with novel ranking-based metrics, provide a comprehensive assessment of recommendation quality. In this paper we outline cutting-edge CF techniques, showcasing their mechanisms and applications and were able to achieve accurate recommendations of almost 90% using MAE and RMSE metrics. By integrating latent factor modeling, sparsity mitigation, contextual enrichment, and advanced evaluation, it paves the way for the next generation of personalized recommendation systems, tailored to meet evolving demands in modern information environments.

IUAutoTimeSVD++: A Hybrid Temporal Recommender System Integrating Item and User Features Using a Contractive Autoencoder

Collaborative filtering (CF), a fundamental technique in personalized Recommender Systems, operates by leveraging user–item preference interactions. Matrix factorization remains one of the most prevalent CF-based methods. However, recent advancements in deep learning have spurred the development of hybrid models, which extend matrix factorization, particularly with autoencoders, to capture nonlinear item relationships. Despite these advancements, many proposed models often neglect dynamic changes in the rating process and overlook user features. This paper introduces IUAutoTimeSVD++, a novel hybrid model that builds upon autoTimeSVD++. By incorporating item–user features into the timeSVD++ framework, the proposed model aims to address the static nature and sparsity issues inherent in existing models. Our model utilizes a contractive autoencoder (CAE) to enhance the capacity to capture a robust and stable representation of user-specific and item-specific features, accommodating temporal variations in user preferences and leveraging item characteristics. Experimental results on two public datasets demonstrate IUAutoTimeSVD++’s superiority over baseline models, affirming its effectiveness in capturing and utilizing user and item features for temporally adaptive recommendations.

Assessing the impact of a personalised application-based nutrition intervention on carbohydrate intake in rural Benin

BackgroundThis study aims to assess the effect of a personalised nutrition intervention on nutrient intake in rural Benin as a tool to tackle the double burden of malnutrition. The personalised recommender system uses information from the eating behaviour of all household members combined to provide tailored advice on adjusting the consumption of up to three food items to tackle malnutrition and obesity in the household. Many developing countries nowadays experience the double burden of malnutrition, the coexistence of undernutrition alongside overweight and obesity, as well as other related non-communicable diseases. Personalised nutrition was very effective in European studies in improving nutrition and tackling obesity, which is why this study aims to translate personalised nutrition to a developing country context.ResultsA study was conducted in rural areas of Benin where 720 households were randomly selected. Due to high attrition, we used propensity score matching and looked into average treatment effects. We found that the recommendation to eat less carbohydrates resulted in a reduction in carbohydrate consumption with a significant effect for both the average treatment effect (ATE) of the whole population as well as the average treatment effect of the treated (ATET). We found that households that received the treatment to consume less food items with a high carbohydrate intake have followed this advice and have consumed on average lower levels of carbohydrates than their control counterparts.ConclusionsRising obesity is a worldwide problem that poses a severe challenge for policymakers. Especially in developing countries the change from too little, to too much is seamless. For the increasing obese population, the recommender system could be a useful tool. The idea of personalised nutrition has the potential to be one of the necessary steps in the ongoing battle against obesity and unhealthy diets. The personalised application-based recommender system used in this study has the ability to be a strong and effective tool for policymakers in the ongoing battle of food security vs. obesity in Benin and other countries. We propose that future research focuses more on personalised nutrition in the context of a developing country.

Implementation of Cloud Run and Cloud Storage as REST API Service on OutfitHub Application

The development of Cloud Computing technology has progressed rapidly in recent years especially with the emergence of Google Cloud Services (GCR) which has become one of the leading cloud service providers. This research focuses on the OutfitHub application, which plays a role in assisting users in determining clothing styles using a personalized recommendation system. In developing this application, the research seeks to implement cloud computing services to improve application performance. The purpose of this research is to implement Cloud Computing, especially Cloud run and Cloud Storage services as Rest API in the Outfithub application. By implementing these two services, it is expected that there is no need to pay attention to the problem of Storage needs that are growing at any time and no need to worry about the need for server configuration because both of these things will be fully done by GCR. Implementing Cloud Computing will provide a variety of benefits in addition to those previously mentioned, such as: being able to access data from anywhere and at any time. This implementation is expected to be able to run OutfitHub applications in a Cloud environment in a serverless computing manner without requiring the design of unnecessary virtual machines.

Effects of Personalized Recommendation System Based on User Learning Behavior in English-Chinese Translation

Users may receive personalised information services and decision support from personalised recommendations. In this paper, a hybrid algorithm-based personalised recommendation approach for learning English is proposed. The user model is created by merging user interest tags, and the Person Rank algorithm is then recommended based on user information. Second, the question-and-answer model is created once the question-and-answer data has been labelled, and the Problem Rank algorithm is suggested in accordance with the question-and-answer data. Then, the approach of tag-based recommendation, comparable user recommendation, and multi-dimensional sliding window are used to construct the recommendation algorithm model. The experimental findings demonstrate that, following the model’s training with the gradient descent technique, the recommendation accuracy is steady at around 0.78, the suggested information can accommodate users who are learning English, and the personalised recommendation effect is enhanced.

A Study on Data Analytics Techniques Used By Netflix to Personalize Recommendations

This research study investigates the complex processes by which Netflix uses data analytics to personalize suggestions for each user. Knowing the nuances of recommendation algorithms becomes critical as streaming platforms continue to rule the entertainment industry. This study tries to clarify the basic ideas behind personalized content suggestions by thoroughly examining Netflix's approach, including algorithms, data collection techniques, and user engagement measures. It also explores the moral ramifications and privacy issues related to these data-driven behaviors. This study provides insights into how personalized recommendation systems will develop in the digital age by examining the effects on user satisfaction, retention, and overall business success. KEY WORDS Data analytics, recommendation algorithm, user engagement, personalized content.

Dual-view multi-modal contrastive learning for graph-based recommender systems

Personalized recommender systems play a crucial role in various online content-sharing platforms (e.g., TikTok). The learning of representations for multi-modal content is pivotal in current graph-based recommender systems. Existing works aim to enhance recommendation accuracy by leveraging multi-modal features (e.g., image, sound, text) as side information for items. However, this approach falls short in fully discerning users' fine-grained preferences across different modalities. To tackle this limitation, this paper introduces the Dual-view Multi-Modal contrastive learning Recommendation model (DMM-Rec). DMM-Rec employs self-supervised learning to guide the learning of user and item representations within the multi-modal context. Specifically, to capture users' preferences for different modalities, we propose specific-modal contrastive learning. Simultaneously, to capture users' cross-modal preferences, cross-modal contrastive learning is introduced to uncover interdependencies in users' preferences across modalities. The contrastive learning tasks not only adaptively explore potential relations between modalities but also address the data sparsity challenge in recommender systems. Extensive experiments conducted on three datasets and compared against ten baselines demonstrate that DMM-Rec outperforms the strongest baseline by an average of 6.81%. These results underscore the effectiveness of considering multi-modal content in improving recommender systems.