Personalized Recommendation System Research Articles

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.

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.

Influential Exemplar Replay for Incremental Learning in Recommender Systems

Personalized recommender systems have found widespread applications for effective information filtering. Conventional models engage in knowledge mining within the static setting to reconstruct singular historical data. Nonetheless, the dynamics of real-world environments are in a constant state of flux, rendering acquired model knowledge inadequate for accommodating emergent trends and thus leading to notable recommendation performance decline. Given the typically prohibitive cost of exhaustive model retraining, it has emerged to study incremental learning for recommender systems with ever-growing data. In this paper, we propose an effective model-agnostic framework, namely INFluential Exemplar Replay (INFER). INFER facilitates recommender models in retaining the earlier assimilated knowledge, e.g., users' enduring preferences, while concurrently accommodating evolving trends manifested in users' new interaction behaviors. We commence with a vanilla implementation that centers on identifying the most representative data samples for effective consolidation of early knowledge. Subsequently, we propose an advanced solution, namely INFERONCE, to optimize the computational overhead associated with the vanilla implementation. Extensive experiments on four prototypical backbone models, two classic recommendation tasks, and four widely used benchmarks consistently demonstrate the effectiveness of our method as well as its compatibility for extending to several incremental recommender models.

Examining link prediction and node connectivity objectives in social networks: Comprehensive review

A social network plays a pivotal role in modern society, facilitating communication, information dissemination and social interactions. As systems such as this one continues to grow in complexity and scope, yet the challenges become identifying the absence of a node as well as ensuring an uninterrupted connection. These comprehensive reviews analyze the objectives of link prediction and node connectivity in the context of social networks. The objective of link prediction and node connectivity in social networks is to pave the way for future analysis in network design and security. This review analyze the node connectivity and link prediction, which is represented as graph G= (s, e) where s denotes the number of vertices and e denotes the number of edges. It highlights the effectiveness of linkages along with node connectivity and forecasting future interactions in enabling the development of targeted marketing strategies and personalized recommendation systems. The significance of node connectivity analysis is to evaluate the resilience and robustness of social networks. It explores the identification of critical nodes and the impact of their removal on network fragmentation, thus emphasizing the importance of developing effective strategies to enhance network resilience and security. Furthermore, the review emphasizes the interplay between link prediction and node connectivity in advancements of link prediction techniques that can booster the accuracy of node connectivity in social networks. It underscores the implications to understand the network dynamics as well as predicting the development of security and efficient social network infrastructures.

Optimizing Matrix Factorization for Personalized Recommendations Using Ridge Regularization

Personalized recommendation systems have become indispensable tools for enhancing user engagement and satisfaction in various online platforms. Matrix Factorization (MF) algorithms serve as fundamental techniques in these systems, allowing for the efficient modeling of user-item interactions and the generation of tailored recommendations. However, ensuring the robustness and generalization capability of MF models remains a challenge, particularly in the presence of sparse and noisy datasets. In this study, we focus on optimizing MF for personalized recommendations through the incorporation of L2 regularization techniques. By introducing a penalty term based on the squared Frobenius norm of the user and item matrices, L2 regularization promotes the learning of more stable and generalized latent representations, thereby mitigating overfitting. We aim to investigate the impact of L2 regularization on recommendation performance and to demonstrate its effectiveness in improving the accuracy and robustness of MF-based recommendation systems. We conduct comprehensive experiments on real-world datasets, evaluating the performance of L2-regularized MF models against baseline approaches. Our results indicate that L2 regularization significantly enhances recommendation accuracy and generalization performance, highlighting its potential to optimize MF for personalized recommendations in diverse application domains

Research on the Application of Big Data and Artificial Intelligence in Search Engine

With the development of the Internet, search engine as an important tool of information retrieval, more and more need to deal with large-scale, complex data. The combination of big data and artificial intelligence technology provides a new solution for search engine optimization. This paper mainly discusses the application of big data and artificial intelligence in search engine, including data collection and processing, data mining and pattern recognition, user behavior analysis, personalized recommendation algorithm, machine learning algorithm, deep learning algorithm, reinforcement learning algorithm and the intelligence of personalized recommendation system.

A collaborative filtering method by fusion of facial information features

Personalized recommendation systems fundamentally assess user preferences as a reflection of their emotional responses to items. Traditional recommendation algorithms, focusing primarily on numerical processing, often overlook emotional factors, leading to reduced accuracy and limited application scenarios. This paper introduces a collaborative filtering recommendation method that integrates features of facial information, derived from emotions extracted from such data. Upon user authorization for camera usage, the system captures facial information features. Owing to the diversity in facial information, deep learning methods classify these features, employing the classification results as emotional labels. This approach calculates the similarity between emotional and item labels, reducing the ambiguity inherent in facial information features. The fusion process of facial information takes into account the user’s emotional state prior to item interaction, which might influence the emotions generated during the interaction. Variance is utilized to measure emotional fluctuations, thereby circumventing misjudgments caused by sustained non-interactive emotions. In selecting the nearest neighboring users, the method considers not only the similarity in user ratings but also in their emotional responses. Tests conducted using the Movielens dataset reveal that the proposed method, modeling facial features, more effectively aligns recommendations with user preferences and significantly enhances the algorithm’s performance.

Optimized Attention Enhanced Temporal Graph Convolutional Network Espoused Research of Intelligent Customer Service System based on Natural Language Processing Technology

ABSTRACT Consumers have begun to move their attention away from product functioning and toward value probably extracted from items. Companies have begun to use customer service systems (CSS) in response to this trend, which are business models that give clients with not solitary tangible items as well as intangible facilities. Even with substantial investigation on Smart CSS frameworks, rare of this frameworks considered customers active data producers actively creating data for the Smart CSS. Furthermore, the majority of them offered a generic remedy rather than a personalized one. To classify customer service systems, performance metrics, such as precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time, and RoC are considered. The performance of AETGCN-NGOA-CSS approach attains 19.11%, 24.12% and 28.13% high specificity, 24.93%, 23.04%, and 9.51% lower computation time, 15.2%, 25.45% and 13.91% higher ROC and 8.45%, 20.98%, and 27.55% higher accuracy compared with existing methods, such as developing personalized recommendation system in smart product service system depend on unsupervised learning model (CSS-BERT), Cognitive Decision-Making approaches in Data-driven Retail Intelligence: Consumer Sentiments, Choices, Shopping Behaviors (CSS-CDMA), e-Commerce Online Intelligent Customer Service System under Fuzzy Control (CSS-FFNN), respectively.

On Item-Sampling Evaluation for Recommender System

Personalized recommender systems play a crucial role in modern society, especially in e-commerce, news, and ads areas. Correctly evaluating and comparing candidate recommendation models is as essential as constructing ones. The common offline evaluation strategy is holding out some user-interacted items from training data and evaluating the performance of recommendation models based on how many items they can retrieve. Specifically, for any hold-out item or so-called target item for a user, the recommendation models try to predict the probability that the user would interact with the item and rank it among overall items, which is called global evaluation . Intuitively, a good recommendation model would assign high probabilities to such hold-out/target items. Based on the specific ranks, some metrics like Recall@K and NDCG@K can be calculated to further quantify the quality of the recommender model. Instead of ranking the target items among all items, Koren first proposed to rank them among a small sampled set of items , then quantified the performance of the models, which is called sampling evaluation . Ever since then, there has been a large amount of work adopting sampling evaluation due to its efficiency and frugality. In recent work, Rendle and Krichene argued that the sampling evaluation is “inconsistent” with respect to a global evaluation in terms of offline top- K metrics. In this work, we first investigate the “inconsistent” phenomenon by taking a glance at the connections between sampling evaluation and global evaluation. We reveal the approximately linear relationship between sampling with respect to its global counterpart in terms of the top- K Recall metric. Second, we propose a new statistical perspective of the sampling evaluation—to estimate the global rank distribution of the entire population. After the estimated rank distribution is obtained, the approximation of the global metric can be further derived. Third, we extend the work of Krichene and Rendle, directly optimizing the error with ground truth, providing not only a comprehensive empirical study but also a rigorous theoretical understanding of the proposed metric estimators. To address the “blind spot” issue, where accurately estimating metrics for small top- K values in sampling evaluation is challenging, we propose a novel adaptive sampling method that generalizes the expectation-maximization algorithm to this setting. Last but not least, we also study the user sampling evaluation effect. This series of works outlines a clear roadmap for sampling evaluation and establishes a foundational theoretical framework. Extensive empirical studies validate the reliability of the sampling methods presented.

Review Paper: CafeEase : Online Food Ordering System

With the rapid digitization of the food industry, online food ordering systems have become an integral part of modern dining experiences. This research paper explores the various facets of online food ordering systems, aiming to enhance the overall user experience through a comprehensive analysis and the development of an innovative design framework. The study begins by conducting a critical review of existing online food ordering platforms, assessing their strengths, weaknesses, and user feedback. By leveraging user-centered design principles, the research identifies key challenges such as user interface complexity, order accuracy, and delivery efficiency. Through a combination of user surveys, usability testing, and data analytics, the paper provides valuable insights into user preferences, behavior patterns, and expectations. Subsequently, the research introduces a novel design framework that integrates cutting-edge technologies, including artificial intelligence, machine learning, and personalized recommendation systems. This framework aims to address identified challenges and elevate the user experience by streamlining the ordering process, enhancing menu navigation, and ensuring accurate order fulfillment. Additionally, the proposed framework prioritizes accessibility, inclusivity, and responsiveness across various devices to cater to a diverse user base. In conclusion, this research paper offers a comprehensive examination of online food ordering systems, proposing an innovative design framework to address existing challenges and enhance the overall user experience. The findings aim to guide future developments in the field, fostering a seamless and enjoyable online dining experience for users worldwide. Key Words: Online Ordering System,,User-friendly interface, Hassle Free Environment, Queless Ordering System

Prerequisite-Enhanced Category-Aware Graph Neural Networks for Course Recommendation

The rapid development of Massive Open Online Courses (MOOCs) platforms has created an urgent need for an efficient personalized course recommender system that can assist learners of all backgrounds and levels of knowledge in selecting appropriate courses. Currently, most existing methods utilize a sequential recommendation paradigm that captures the user’s learning interests from their learning history, typically through recurrent or graph neural networks. However, fewer studies have explored how to incorporate principles of human learning at both the course and category levels to enhance course recommendations. In this article, we aim at addressing this gap by introducing a novel model, named Prerequisite-Enhanced Catory-Aware Graph Neural Network (PCGNN), for course recommendation. Specifically, we first construct a course prerequisite graph that reflects the human learning principles and further pre-train the course prerequisite relationships as the base embeddings for courses and categories. Then, to capture the user’s complex learning patterns, we build an item graph and a category graph from the user’s historical learning records, respectively: (1) the item graph reflects the course-level local learning transition patterns and (2) the category graph provides insight into the user’s long-term learning interest. Correspondingly, we propose a user interest encoder that employs a gated graph neural network to learn the course-level user interest embedding and design a category transition pattern encoder that utilizes GRU to yield the category-level user interest embedding. Finally, the two fine-grained user interest embeddings are fused to achieve precise course prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of PCGNN compared with other state-of-the-art methods.

Research on the application of personalized recommendation

With the development of the Internet, people have more opportunities to be exposed to personalized recommendations, and the heat of related topics has increased. The purpose of this paper is to summarize the application principle and use of personalized recommendations by analyzing the relevant data, so that people can have a better understanding of personalized recommendations and make preparations for future related research. This paper first introduces the general development context of the personalized recommendation system and its uniqueness. The analysis finds that personalized recommendations can provide users with the most relevant and valuable information, goods, or services according to their interests, preferences, and behaviors. Secondly, this paper explores the application scenarios of personalized recommendation systems in different fields, including but not limited to video content, e-commerce, and online learning. In addition, this paper also lists the application of YouTube and Netflix in video content recommendation, as well as personalized recommendation services in e-commerce and online learning platforms. In particular, the rise of intelligent adaptive online learning systems and their adaptability in the field of education and training are analyzed in detail. Through this research, it can be found that a personalized recommendation system is very beneficial to people's production and life, but at present, personalized recommendation is still in the development stage, and there are many problems to be solved.

Position information visualization analysis and personalized recommendation based on ant colony

With the rapid development of network technology, online recruitment and job hunting have become an important way of job hunting at present, but job seekers spend a lot of time looking for suitable positions in the face of massive job information. Traditional artificial selection of job information is difficult to solve the problem of job seekers finding suitable positions quickly and accurately. This article is based on ant colony algorithm for visual analysis and personalized recommendation of job information. Through visual analysis of massive job information on the network, personalized recommendations are made based on job seekers' professional, skill, behavior, and other information. A visual analysis and personalized recommendation system for job information is established, and recommendation accuracy, efficiency, and recall rate are evaluated and analyzed using recommendation theory, realize comprehensive evaluation of information visualization analysis and personalized recommendation quality of position information based on ant colony algorithm. Compared with artificial selection of position information, it is fast and highly matched.

The investigation and application of Muti-Armed algorithms in recommendation systems

In the contemporary Internet recommendation systems in various fields, muti-armed algorithms will show high accuracy and practicability. Internet users can always get abundant positive feedback through the recommendation system via utilizing these algorithms. In this paper, there are three most typical muti-armed algorithms as examples are provided. Explore-Then-Commit (ETC) algorithm is the first to mentioned. The physical meaning of this algorithm is that in the exploration stage, the action will be selected in a certain order, and after a certain number of rounds, the action with the largest average reward will be directly selected. Moreover, Upper Confidence Bond (UCB) algorithm is also a type of pivotal tool. The main function of UCB algorithm is to make the selection by calculating the upper bound of each arm confidence interval instead of the expected reward of the slot machine. Thus, it is an optimistic algorithm. First, select each arm in random order, then calculate the value of the upper bound of each arm confidence interval, and finally, select the arm with the largest value. The last mentioned Thompson Sampling (TS) Algorithm take Bayesian optimization as the theoretical basis. This algorithm takes out the candidate parameters, generates a random number, and selects the maximum value for input. This paper also introduced two fields related to the topic of the application of muti-armed algorithms in the recommendation systems in modern fields. News recommendation algorithms and personalized recommendation systems can be more comprehensive and representative to illustrate the practicality of these algorithms. Therefore, muti-armed algorithms reflect the importance in the recommendation fields via the balance of exploration and exploitation.

FedSarah: A Novel Low-Latency Federated Learning Algorithm for Consumer-Centric Personalized Recommendation Systems

FedSarah: A Novel Low-Latency Federated Learning Algorithm for Consumer-Centric Personalized Recommendation Systems

Federated Learning-Based Personalized Recommendation Systems: An Overview on Security and Privacy Challenges

Federated Learning-Based Personalized Recommendation Systems: An Overview on Security and Privacy Challenges