Personalized Recommendation System Research Articles

Integrating User Profiles and Collaborative Filtering for Smart Recommendation of Tourism City Cultural and Creative Products

In the domain of tourism and cultural creative product recommendations, the design and implementation of personalized recommendation systems are crucial for enhancing user satisfaction and engagement. Although traditional recommendation systems, such as Collaborative Filtering techniques, have been widely applied, they still face challenges in dealing with sparse data and achieving highly personalized recommendations. To solve this problem, a collaborative filtering recommendation method combining user profiles and multi-task knowledge acquisition is proposed in this study. We first construct consumer profiles with the aid of studying users behavioral facts and demographic statistics, after which layout a multi-task learning framework that improves the accuracy and personalization degree of tips through getting to know common functions of various however associated recommendation duties through shared representations. We evaluated our approach on numerous actual-international datasets, and the experimental outcomes display sizable improvements over conventional collaborative filtering strategies and other existing recommendation systems in key overall performance signs along with accuracy, recall, and F1-score. These results indicate that the collaborative filtering advice approach is more advantageous via the mixing of person profiles and multi-venture learning holds sizable software potential inside the subject of tourism metropolis cultural and innovative product recommendations.

Optimization Strategies in Consumer Choice Behavior for Personalized Recommendation Systems Based on Deep Reinforcement Learning

In domains such as e-commerce and media recommendations, personalized recommendation systems effectively alleviate the issue of information overload. However, existing systems still face challenges in multimodal data processing, data sparsity, and dynamic changes in user preferences. This paper proposes a Hierarchical Generative Reinforcement Learning Recommendation Optimization framework (HG-RLRO) that addresses these issues by integrating multimodal data, Generative Adversarial Networks (GAN), Inverse Reinforcement Learning (IRL), and Hierarchical Temporal Difference Learning (HTD). HG-RLRO employs a multi-agent architecture to handle textual and image data and utilizes GAN to generate simulated user behavior data to mitigate data sparsity. IRL dynamically infers user preferences across multiple time scales.

The Analysis of Emotion-Aware Personalized Recommendations via Multimodal Data Fusion in the Field of Art

This paper proposes an emotion-aware personalized recommendation system (EPR-IoT) based on IoT data and multimodal emotion fusion, aiming to address the limitations of traditional recommendation systems in capturing users' emotional states of artistic product consumption in real time. With the proliferation of smart devices, physiological signals such as heart rate and skin conductance—which are strongly correlated with emotional states—provide new opportunities for emotion recognition. For example, an increase in heart rate is typically associated with emotions like anxiety, anger, or fear, while a decrease is linked to emotional states like relaxation or joy. Similarly, skin conductance rises with emotional arousal, particularly during stress or fear. These physiological signals, combined with text, speech, and video data of art products, are fused to construct an art emotion-driven recommendation model capable of dynamically adjusting the recommended content.

Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement Learning

Continuously learning new skills is essential for talents to gain a competitive advantage in the labor market. Despite extensive efforts on relevance- or preference-based skill recommendations, little attention has been given to the practical effects of job skills in the market. To bridge this gap, we propose an explainable personalized skill learning recommendation system that considers the long-term learning benefits and costs. Specifically, we model skill learning utilities based on salary and learning cost associated with job positions and propose a multi-objective deep reinforcement learning framework to model and maximize long-term utilities. Furthermore, we propose a Self-explaining Skill Recommendation Deep Q-network (SeSRDQN) that captures and prototypes prevalent skill sets in the market into representative exemplars for decision-making. SeSRDQN quantitatively decomposes the talent’s long-term learning utility into contributions from each exemplar, offering a comprehensive and multi-factorial explanation across various skill learning options. To tackle the combinatorial complexity of the skill space, we develop an MCTS-based optimization-decoding iterative training procedure for explanation fidelity and human understandability. In this way, talents will receive a tailored roadmap of essential skills, complemented by exemplar-based explanations, to effectively plan their careers. Extensive experiments on a real-world dataset validate the effectiveness and explainability of our approach.

Personalized recommendation system to handle skin cancer at early stage based on hybrid model

ABSTRACT Skin cancer is one of the most prevalent and harmful forms of cancer, with early detection being crucial for successful treatment outcomes. However, current skin cancer detection methods often suffer from limitations such as reliance on manual inspection by clinicians, inconsistency in diagnostic accuracy, and a lack of personalized recommendations based on patient-specific data. In our work, we presented a Personalized Recommendation System to handle Skin Cancer at an early stage based on Hybrid Model (PRSSCHM). Preprocessing, improved deep joint segmentation, feature extraction, and classification are the major steps to identify the stages of skin cancer. The input image is first preprocessed using the Gaussian filtering method. Improved deep joint segmentation is employed to segment the preprocessed image. A set of features including Median Binary Pattern (MBP), Gray Level Co-occurrence Matrix (GLCM), and Improved Local Direction Texture Pattern (ILDTP) are extracted in the next step. Finally, the hybrid classification includes Improved Bi-directional Long Short-Term Memory (Bi-LSTM) and Deep Belief Network (DBN) used for the classification process, where the training will be carried out by the Integrated Bald Eagle and Average and Subtraction Optimizer (IBEASO) algorithm via optimizing the weights of the models.

Comparative Analysis of Multi-armed Bandits Models for Recommendation Systems

With the booming development of personalized recommendation systems, numerous algorithms have been designed to bring more bliss and engagement to users. Multi-armed bandits (MAB) models have been increasingly used in this context, as they appropriately balance exploration and exploitation. This work provides an in-depth comparative study of diverse MAB algorithms like -greedy, Upper Confidence Bound (UCB1), and Thompson Sampling in the recommendation systems literature. This paper benchmark the computational efficiency, cumulative reward, and adaptability of these models by running them at interactive speeds with simulations of real-world user interactions. The results suggest that UCB1 works well in stable environments, whereas Thompson Sampling excels in volatile settings. The paper examines the attributes of these MAB algorithms through a systematic review of recent research. The paper also discusses the utility of MAB models in areas such as online advertising, streaming services, and e-commerce. Future research will likely focus on integrating deep learning approaches with MAB to further improve recommendation systems.

Enhancing E-Commerce Recommendations Through Data-Driven Approaches: A Case Study of Amazon Product Reviews

In the dynamic landscape of e-commerce, personalized product recommendation systems are pivotal in enhancing user experiences and driving business success. This study leverages the Amazon Product Reviews dataset, a rich source of user-generated feedback, to design a scalable and effective personalized recommendation system. Adopting a structured methodology encompassing four data analysis phases includes descriptive, diagnostic, predictive, and prescriptive. This research extracts meaningful insights from product reviews and ratings. The study captures user preferences and sentiments using advanced natural language processing (NLP) and machine learning techniques, including sentiment analysis and hybrid recommendation models. Implementing distributed computing frameworks like Apache Spark ensures scalability and operational efficiency. Centered on the electronics category, this research integrates sentiment insights with collaborative and content-based filtering techniques to address challenges like data sparsity and the cold-start problem. The findings contribute to advancing personalized recommendation systems by delivering actionable insights that enhance customer satisfaction, streamline product discovery, and provide significant value to academic research and industry practices.

Optimizing personalized psychological well-being interventions through digital phenotyping: results from a randomized non-clinical trial.

Digital technologies, including smartphones, hold great promise for expanding mental health services and improving access to care. Digital phenotyping, which involves the collection of behavioral and physiological data using smartphones, offers a novel way to understand and monitor mental health. This study examines the feasibility of a psychological well-being program using a telegram-integrated chatbot for digital phenotyping. A one-month randomized non-clinical trial was conducted with 81 young adults aged 18-35 from Italy and the canton of Ticino, a region in southern Switzerland. Participants were randomized to an experimental group that interacted with a chatbot, or to a control group that received general information on psychological well-being. The chatbot collected real-time data on participants' well-being such as user-chatbot interactions, responses to exercises, and emotional and behavioral metrics. A clustering algorithm created a user profile and content recommendation system to provide personalized exercises based on users' responses. Four distinct clusters of participants emerged, based on factors such as online alerts, social media use, insomnia, attention and energy levels. Participants in the experimental group reported improvements in well-being and found the personalized exercises, recommended by the clustering algorithm useful. The study demonstrates the feasibility of a digital phenotyping-based well-being program using a chatbot. Despite limitations such as a small sample size and short study duration, the findings suggest that digital phenotyping and personalized recommendation systems could improve mental health care. Future research should include larger samples and longer follow-up periods to validate these findings and explore clinical applications.

Research on the Analysis of students’ English Learning Behavior and Personalized Recommendation Algorithm based on Machine learning

The goal of this study is to create a personalized recommendation system for English learning resources by analysing students’ English learning behaviors using a Generalized Regression Neural Network (GRNN). For efficient language acquisition, tailored educational support is essential due to the diversity of students’ linguistic backgrounds and learning demands. Performance ratings, personal preferences, and the amount of time students spent on various content categories were among the data gathered for this study on how students interacted with English language learning materials. Our initial analysis of the patterns and discrepancies in the learning behaviors of the pupils involved the use of the GRNN model. Strong insights into the correlations between various aspects and learning results were obtained by the neural network, which was especially well-suited for this investigation due to its affinity for handling non-linear interactions and its low need for preprocessing data. These observations led us to create an individual recommendation engine that recommends educational resources and activities based on each user’s learning preferences and skill level. Using a varied set of students, a controlled study was conducted to assess the efficacy of the individual suggestions. Comparing the preliminary findings to conventional, non-personalized methods of learning, efficiency in learning and student engagement have significantly improved. In addition to showcasing GRNN’s potential for educational applications, this work offers an adaptable framework for adaptive learning systems across a range of academic fields.

Artificial Intelligence in the Cultural Industry: Applications and Challenges

This study provides an in-depth analysis of the applications of Artificial Intelligence (AI) in the cultural industry and the challenges and opportunities it brings. Through literature review, case analysis, and expert interviews, this research reveals the key role of AI technology in content creation, cultural heritage protection, personalized recommendation systems, and market analysis. The study points out that while AI has brought innovation and efficiency to the cultural industry, it also faces technological challenges, ethical and legal issues, market acceptance, and consumer trust. This research proposes a series of strategic recommendations, including strengthening technological innovation, improving copyright protection mechanisms, enhancing market segmentation and consumer insights, adapt to digital transformation with cultivation talents. The results of the study provide valuable insights and guidance for policymakers, cultural industry practitioners, and academic researchers.

Advanced Digital Technologies for Promoting Indian Culture and Tourism through Cinema

Abstract: Culture and Tourism are two mainly interrelated elements that contribute a lot to achieving Sustainable Development for any developing country especially India, which has an extremely rich historical and cultural background. Tourism Industry is the fastest growing sector in a local economy creating several job opportunities which ultimately raise the standard of living of people which further raises the consumption level of goods and services, resulting in a rise in the Gross Domestic Product (GDP) of a country. However, various studies pointed out major promotional strategies concerning tourism and culture but an amalgamated promotional approach for both was still missing. With this motivation, the current study aims at providing an amalgamated promotional approach in assimilation with the latest Industry 4.0 technologies such as Artificial Intelligence (AI), Machine Learning (ML), Big Data, Blockchain, Virtual Reality (VR), Digital Twin and Metaverse to the Indian tourism industry by reviewing prior research studies. The findings of the current study are establishing an online future travel demands forecasting system, an online tourists’ destination personalized recommendation system, an online tourist’s review analysis recommendation system, and an online destination image recommendation system and provide the practical design for it through 1+5 Architectural Views Model and by applying several ML algorithms such as CNN, BPNN, SVM, Collaborative Filtering, K-means Clustering, API Emotion, and Naïve Bayes algorithms. Finally, this study has discussed challenges and suggested vital recommendations for future work with the assimilation of Industry 4.0 technologies.

Increasing the Effectiveness of Personalized Recommender Systems Based on the Integrated GNN-RL Model

Increasing the Effectiveness of Personalized Recommender Systems Based on the Integrated GNN-RL Model

An E-Commerce Recommendation System Based on Dynamic Analysis of Customer Behavior

The expansion of e-commerce site platforms has brought about the need for personalized recommendation systems for the betterment of user experience and sales. In this paper, we propose a new approach for the recommendation system which is based on the dynamic behavior analysis of the users of the system. This involves analytics on the current data about customer activities such as buying history, browsing history, and likes to give recommendations that are up to date and very precise. Static models of the recommendation systems are contrasted to dynamic ones with the emphasis on the benefits of using for example collaborative filtering, content based filtering and other machine learning based hybrid systems to such systems. The performance metrics of accuracy, recall and F1 score confirm that the implementation of the system as proposed solves the cold start problem, enhances the scalability of the system and improves the accuracy of recommendations made. This work therefore opens the door to next generation recommendation engines which are more e-commerce oriented and can respond to the changing needs of customers more effectively.

Enhancing User Experience through Machine Learning-Based Personalized Recommendation Systems: Behavior Data-Driven UI Design

The application of artificial intelligence (AI) continues to expand across various industries, especially in enhancing user experience and optimizing business processes. Through deep learning and machine learning algorithms, companies are able to analyze user behavior data and provide personalized recommendations, which effectively improve customer satisfaction and loyalty. This data-driven approach enables businesses to stand out in a highly competitive market. This paper explores the key role of machine learning-based personalized recommendation systems in improving user experience and highlights the importance of behavioral data-driven UI design for business success. Research shows that successful recommendation systems not only rely on advanced technology applications, but also need to deeply understand user needs to optimize user interface design and promote effective user interaction. As technology continues to advance, personalized recommendation systems will become more intelligent, and companies should actively explore these innovative ways to increase user engagement and brand loyalty to achieve sustainable business growth.

Movie Recommendation System Based on Collaborative Filtering Network

Abstract. In the era of big data, personalized recommendation systems have become crucial for enhancing user experience and improving information retrieval efficiency, especially in the movie recommendation domain. This study proposes a method based on Collaborative Filtering Network (CFN), utilizing deep learning techniques to construct an efficient and accurate movie recommendation system. We evaluated the model's effectiveness using the MovieLens dataset. The experimental results demonstrate that the proposed CFN model performs well across multiple metrics, including Hit Ratio, providing new insights for research in the recommendation system field.

PERS: A Personalized Recommender System for Student-Generated Questions in Programming Courses

PERS: A Personalized Recommender System for Student-Generated Questions in Programming Courses

An explainable graph-based course recommendation model based on multiple interest factors

Personalized recommendation systems are increasingly being employed on massive open online course platforms to address the issue of information overload in online learning. At present, there is a growing trend of utilizing graph-structured data, including knowledge graphs and user-course bipartite graphs, to improve the recommendation quality. However, existing models still encounter specific limitations. To overcome these constraints, this paper introduces a novel dual-end course recommendation model based on learner-course bipartite and course knowledge graphs and employs a rule-based approach to delve into the profound correlations between learners and courses, resulting in an interpretable course recommendation model. Empirical tests conducted on the large-scale real-world MOOCCube dataset show that the proposed model outperforms all state-of-the-art reference methods. In addition, a case study illustrates the interpretability of the recommendation outcomes. The code and dataset are available at: https://github.com/miaomiao924/IDRMI.git.

Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition

Automatic clothes pattern recognition is important to assist visually impaired people and for real-world applications such as e-commerce or personal fashion recommendation systems, and it has attracted increased interest from researchers. It is a challenging texture classification problem in that even images of the same texture class expose a high degree of intraclass variations. Moreover, images of clothes patterns may be taken in an unconstrained illumination environment. Machine learning methods proposed for this problem mostly rely on handcrafted features and traditional classification methods. The research works that utilize the deep learning approach result in poor recognition performance. We propose a deep learning method based on an ensemble of convolutional neural networks where feature engineering is not required while extracting robust local and global features of clothes patterns. The ensemble classifier employs a pre-trained ResNet50 with a non-local (NL) block, a squeeze-and-excitation (SE) block, and a coordinate attention (CA) block as base learners. To fuse the individual decisions of the base learners, we introduce a simple and effective fusing technique based on entropy voting, which incorporates the uncertainties in the decisions of base learners. We validate the proposed method on benchmark datasets for clothes patterns that have six categories: solid, striped, checkered, dotted, zigzag, and floral. The proposed method achieves promising results for limited computational and data resources. In terms of accuracy, it achieves 98.18% for the GoogleClothingDataset and 96.03% for the CCYN dataset.

A Bibliometric Perspective on Research of Personalized Recommendation Systems and the Filter Bubble Effect in 2024

This research adopts a bibliometric approach to investigate the topic of personalized recommendation systems and the information cocoon effect in 2024. Through a systematic review of literature from specific data sources and the application of diverse analytical methods, it provides a detailed examination of the current state and developmental trends in this field. The analysis encompasses the distribution of core authors, prominent journals, and leading countries, highlighting the main research forces in this domain. Co-occurrence and clustering analyses of keywords offer precise insights into research hotspots and emerging directions. This study not only synthesizes the existing research landscape but also identifies pressing challenges and proposes future directions. It aims to offer a comprehensive and detailed reference to support the sustainable development of this field, promote the optimization of personalized recommendation systems, alleviate the information cocoon effect, and contribute to the creation of a healthy, harmonious, and open information society. The findings are of substantial theoretical and practical significance.

Design and Implementation of Personalized Recommendation Algorithm in Alumni Using Natural Language Processing

Recently, new possibilities for its web portal application have been presented by the automatic discovery of information in educational data, which has been expanding its horizons. The recommendation of university courses for high school students is an unexplored area. Traditional recommendation systems rely on collaborative filtering, which, in this instance, does not apply since the number of items and users needed to achieve high-performance levels is too small. One of the most well-known recommendation approaches in the course recommendation career is Natural Language Processing (NLP). Association rules have been added using data from alumni and assessments of student recommendations. The rules have been created by first mining data about the course that students completed, then comparing the discovered relationships to the existing course tree to improve it. Hence, a Natural Language Processing-based Personalized Recommendation System (NLP-PRS) has been established to help students choose courses that would take them to their desired careers by analyzing data from alumni and job offers. This study can use the alumni web portal and content formation through NLP to extract relevant information from course programs. It then presents recommendations based on students’ performance, interests, and results in each course that makes up each program. In addition, for the best results in improving their learning skills, students’ decisions should inform the recommended compliance. Because students vary in preferences (based on prior knowledge, learning style, emotional state, etc.), it is impossible to recommend a set timeframe for the recommended sequence. Based on the experimental outcome data, it gives the impression that the system is superior for allowing alumni to engage, share updates, and interact with current students who are alumni.