Recommender Systems Research Articles

A Self-Evaluating Collaborative Filtering Recommendation Algorithm: A Case Study of Anime Recommendations

Abstract. This paper introduces a collaborative filtering recommendation algorithm aimed at addressing the issues of information insufficiency and the mismatch of user personalization needs in anime recommendations. Firstly, we review relevant literature to explore the application of collaborative filtering algorithms in recommendation systems and previous research findings. Then, we detail the design and implementation of collaborative filtering algorithms based on anime and user data, calculating similarities between anime and between users respectively for recommendation purposes. Finally, a self-evaluation is conducted to achieve optimal recommendation performance. Experimental results show that when recommending 10 anime titles, the ROC analysis results indicate a high level of precision, with both algorithms performing well in terms of accuracy.

Recommender Systems in the Era of Large Language Models (LLMs)

Recommender Systems in the Era of Large Language Models (LLMs)

Intelligible graph contrastive learning with attention-aware for recommendation

Recommender systems are an important tool for information retrieval, which can aid in the solution of the issue of information overload. Recently, contrastive learning has shown remarkable performance in recommendation by data augmentation processes to address highly sparse data. Our paper proposes an Intelligible Graph Contrastive Learning with attention-aware (IntGCL) for recommendation. Particularly, our IntGCL first introduces a novel attention-aware matrix into graph convolutional networks (GCN) to identify the importance between users and items, which is constructed to preserve the importance between users and items by a random walk with a restart strategy and can enhance the intelligibility of our model. Then, the attention-aware matrix is further utilised to guide the generation of a graph-generative model with attention-aware and a graph-denoising model for automatically generating two trainable contrastive views for data augmentation, which can de-noise and further enhance the intelligibility. Comprehensive experiments on four real-world datasets indicate the superiority of our IntGCL approach over multiple state-of-the-art methods. Our datasets and source code are available at https://github.com/restarthxr/InpGCL.

Improving Conversational Recommender System Via Contextual and Time-Aware Modeling With Less Domain-Specific Knowledge

Improving Conversational Recommender System Via Contextual and Time-Aware Modeling With Less Domain-Specific Knowledge

Toward a Recommender System for Assisting Customers at Risk of Churning in E-commerce Platforms Based on a Combination of Social Network Analysis (SNA) and Deep Learning

Toward a Recommender System for Assisting Customers at Risk of Churning in E-commerce Platforms Based on a Combination of Social Network Analysis (SNA) and Deep Learning

Enhancing Knowledge Sharing on Social Media: The Role of Functionalities in User Engagement and Information Quality

This study investigates functionalities on social media platforms that promote effective knowledge sharing, focusing on information quality and user engagement. A systematic literature review (SLR) was conducted to analyze relevant research. The findings reveal that functionalities like content creation and curation tools, user interaction and feedback mechanisms, community building and gamification elements, and knowledge filtering and recommendation systems all contribute to a positive knowledge-sharing environment. These functionalities empower users to create high-quality content, engage in discussions that refine information, and discover trustworthy sources. The study highlights the potential of social media platforms for knowledge sharing but acknowledges that the effectiveness of functionalities can vary depending on the platform and audience. Future research is recommended to explore the most impactful functionalities in specific contexts and the interplay between functionalities, user behavior, and information quality.

Combining predictive models: Hybrid approaches in crop recommendation systems

Combining predictive models: Hybrid approaches in crop recommendation systems

Advanced Hybrid Recommender Systems: Enhancing Precision and Addressing Cold-Start Challenges in E-Commerce and Content Streaming

Advanced Hybrid Recommender Systems: Enhancing Precision and Addressing Cold-Start Challenges in E-Commerce and Content Streaming

MuSe-CarASTE: A comprehensive dataset for aspect sentiment triplet extraction in automotive review videos

In the Aspect-Based Sentiment Analysis (ABSA) domain, the Aspect Sentiment Triplet Extraction (ASTE) task has emerged as a pivotal endeavor, offering insights into nuanced aspects, opinions, and sentiment relationships. This paper introduces “MuSe-CarASTE”, an extensive and meticulously curated dataset purpose-built to propel ASTE advancements within the automotive domain. The core emphasis of MuSe-CarASTE is on aspect, opinion, and sentiment triplets, facilitating a comprehensive analysis of product reviews. Comprising transcripts from MuSe-Car’s automotive video reviews, MuSe-CarASTE presents a sub-stantial collection of nearly 28,295 sentences organized into 5,500 segments. Each segment is meticulously annotated with multiple aspects, opinions, and sentiment labels, offering unprecedented granularity for ASTE tasks. The percentage agreement between annotated triples by different annotators over the randomly sampled subset of the dataset is 79.74 %, at similarity threshold τ = 0.60. We also experimented with four baseline models on our datset and report results. The distinctiveness of the dataset emerges from its extension into the automotive domain, shedding light on sentiment dynamics specific to vehicles. With the fusion of extensive content and real-world applicability, MuSe-CarASTE presents a fertile ground for Natural Language Processing (NLP) innovation. Researchers, practitioners, and data scientists can harness MuSe-CarASTE to build and evaluate NLP models tailored for challenges in ASTE. These challenges encompass intricate aspect-opinion relationships, multi-word aspect and opinion extraction, and the subtleties of vague language. Moreover, including aspects not verbatim in sentences introduces a practical dimension to our dataset, enabling real-world applications like review pattern analysis, summarization, and recommender system enhancement. As a pioneering benchmark for NLP model evaluation in ABSA, MuSe-CarASTE integrates content richness, real-world context, and sentiment complexity. The integration empowers the development of accurate, adaptable, and insightful sentiment analysis models within the automotive review landscape.

Application of artificial intelligence in career development of women in STEM field: Counseling, network construction and recruitment

With the rapid development of science and technology, the application of artificial intelligence (AI) in STEM (science, technology, engineering and mathematics) is increasingly extensive, which provides a new idea for solving the challenges of women's career development in STEM. This paper deeply discusses the application of AI in counseling, network construction and recruitment of women's STEM career development, aiming at promoting gender equality and women's career growth. In career counseling, AI technology provides customized career advice and development path for women through deep learning and big data analysis. According to personal education background, skill level, hobbies and career goals, the system intelligently recommends suitable career directions, and updates industry trends and job market data in real time to help women make more informed career choices. The AI tutoring system also enhances women's professional self-confidence through functions such as simulated interviews and career planning exercises. In terms of network construction, AI helps women to expand their professional networks more effectively through intelligent recommendation system. Based on user behavior data and preferences, AI accurately recommends industry seminars, technical exchange activities and online communities to promote women to establish contacts with like-minded professionals. AI also assists women in managing and maintaining network relationships, ensures the continuity of network interaction through intelligent reminding functions, and identifies key nodes and influential people to enhance network quality. In the recruitment process, AI technology reduces gender bias and improves the fairness and transparency of recruitment through automatic resume screening, standardized interview process and competency-based evaluation model. AI system can analyze a large amount of data, accurately match the skills and job requirements of female candidates, and improve the employment rate of women in STEM field. At the same time, through transparent algorithm decision-making process and fairness check, the potential gender bias problem in AI algorithm is overcome. The research in this paper provides theoretical support and practical guidance for using AI technology to promote women's career development in STEM field, and helps to build a more equal and inclusive scientific and technological working environment.

The Short Video Platform Recommendation Mechanism Based on the Improved Neural Network Algorithm to the Mainstream Media

Currently, the capacity of short videos continues to increase. Video manufacturers hope to enhance user experience and stickiness through recommendation algorithms, while users seek personalized videos to save time and money. Therefore, in order to address the data sparsity and high-dimensional feature extraction, this study proposes a novel short video platform recommendation model. The proposed method utilizes the term frequency inverse document frequency algorithm for text mining, and combines error back propagation neural network for learning to explore the potential connection between users and videos. This method combines natural language processing and image analysis in deep learning to construct accurate user and video models, deeply explore user interests, and improve the accuracy and effectiveness of recommendation systems for user preferences. The research results showed that the recommendation accuracy of this method was 66% and 70% respectively, and the prediction accuracy was 73.50% and 88% respectively. When Num=128, 200 data points were recommended within 0.3678s. The proposed algorithm outperforms the other three methods in terms of recommendation accuracy compared with the ItemCF and UserCF algorithms. This is because the method uses an approach based on image and user vector, combined with relevant features of the video and user. The proposed method can deeply explore the relevant features of videos and users, overcoming the data scarcity in previous collaborative screening, and guiding video recommendation on practical media platforms.

Design of News Sentiment Classification and Recommendation System Based on Multi-model Fusion and Text Similarity

Design of News Sentiment Classification and Recommendation System Based on Multi-model Fusion and Text Similarity

Asymmetric variable depth learning automaton and its application in defending against selfish mining attacks on bitcoin

Learning Automaton (LA) theory, a branch of reinforcement learning, initially began with the Fixed Structure Learning Automaton (FSLA) family and was later expanded to include the Variable Structure Learning Automaton (VSLA) family. Tuning the depth of FSLA in complex environments has long been a challenging task, significantly limiting the ability to effectively navigate the exploration-exploitation dilemma. No solution has been found for this open problem yet. This study addresses this issue by introducing a novel hybrid learning automaton model called Asymmetric Variable Depth Hybrid LA (AVDHLA). The AVDHLA model intelligently learns the depth of fixed structure LA in an autonomous manner by combining LKN,K from FSLA class and Variable Action Set LA (VASLA) from VSLA class. Computer simulations are conducted to validate the proposed model in diverse environments, including both stationary and non-stationary (Markovian switching and State-dependent) scenarios. Performance evaluation is based on predefined metrics, such as total number of rewards (TNR) and action switching (TNAS). Statistical tests indicate that across both stationary and non-stationary environments, AVDHLA consistently outperforms LKN,K in terms of TNR and TNAS across the majority of experiments. Moreover, the AVDHLA model is applied in two key applications. Firstly, it is used to defend against the selfish mining attack in Bitcoin and is compared with the well-known tie-breaking mechanism. Simulation results consistently demonstrate that our proposed method increases the threshold for successful selfish mining attacks from 25% to 40%. Secondly, the AVDHLA model has been applied to develop a novel learning automaton-based recommendation system. The results demonstrate the superiority of the proposed method in terms of the Click-Through Rate (CTR) and Precision compared to previous approaches.

A comparative study of various recommendation algorithms based on E-commerce big data

With the rapid development of the Internet and the concomitant exponential growth of information, we have entered an era characterized by information overload. The abundance of data has rendered it increasingly arduous for users to pinpoint specific information they require. However, various forms of recommendation algorithms proffer solutions to this challenge. These algorithms predict items or products that may pique users’ interest based on their historical behavior, preferences, and interests. As one of the current hot research fields, recommendation algorithms are extensively employed across E-commerce platforms, movie streaming services, and various other contexts to cater to the diverse needs of users. In this context, a multi-recommendation algorithms comparison platform is proposed, which includes a two-fold model: online evaluation and offline evaluation. Taking the data set of the Chinese Amazon online shopping mall as the experimental data, item-based collaborative filtering (Item-CF) algorithm, content-based (TF-IDF) algorithm, item2vec model, alternating least squares (ALS) algorithm and neural network algorithm are evaluated in the offline model. In the real-time recommendation part, model-based algorithm is used to achieve the users’ rating mechanism. And the metrics used for evaluation include: precision, recall, accuracy and performance. The experimental results show that the average performance of hybrid algorithms such as ALS algorithm and neural network algorithm is higher than that of other traditional algorithms, and the real-time recommendation system achieves the purpose of improving recommendation speed. By integrating various recommender algorithms into the multi-recommendation algorithms comparison platform, this platform automatically computes and presents various performance indicators based on the user-provided dataset. It aids E-commerce platforms in making informed decisions regarding algorithm selection.

Satellite Imagery, Big Data, IoT and Deep Learning Techniques for Wheat Yield Prediction in Morocco

In the domain of efficient management of resources and ensuring nutritional consistency, accuracy in predicting crop yields becomes crucial. The advancement of artificial intelligence techniques, synchronized with satellite imagery, has emerged as a potent approach for forecasting crop yields in modern times. We used two types of data: spatial data and temporal data. Spatial data are gathered from satellite imagery and processed using ArcGIS to extract data about crops based on several indices like NDVI and NWDI. Temporal data are gathered from agricultural sensors such as temperature sensors, rainfall sensor, precipitation sensor and soil moisture sensor. In our case we used Sentinel 2 satellite to extract vegetation indices. We have used IoT systems, especially Raspberry Pi B+ to collect and process data coming from sensors. All data collected are then stored into a NoSQL server to be analysed and processed. Several machine learning and deep learning algorithms have been used for the processing of crop recommendation system, such as logistic regression, KNN, decision tree, support vector machine, LSTM, and Bi-LSTM through the collected dataset. We used GRU deep learning model for the best performance, the RMSE and R2 for this model was 0,00036 and 0,99 respectively.The main contribution of our paper is the development of a new system that can predict several crop yields, such as wheat, maize, etc, using IoT, satellite imagery for spatial data and the use of sensors for temporal data. We are the first paper that has combined spatial data and temporal data to predict crop yield based on deep learning algorithms, unlike other works that uses only remote sensing data or temporal data. We created an E-monitoring crop yield prediction system that helps farmers track all information about crops and show the result of prediction in a mobile application. This system helps farmers with more efficient decision making to enhance crop production. The main production regions of wheat in Morocco are in the rainfed areas of the plains and plateaus of Chaouia, Abda, Haouz, Tadla, Gharb and Saïs. We studied three main regions well known for wheat production which are Rabat-Salé, Fez-Meknes, Casablanca-Settat.

A real-time energy and cost efficient vehicle route assignment neural recommender system

A real-time energy and cost efficient vehicle route assignment neural recommender system

Multidimensional Dynamic Destination Recommender Search System Employing Clustering: A Machine Learning Approach

Objectives: Recommender Systems (RS) powered by algorithms of machine learning is a popular tool for planning and implementing custom-made travel proficiencies. The persistence of this study is to recommend destinations according to a selection of various dimensions by the user. Methods: This approach uses a hybrid filtering system for recommendation with a weighted K-means clustering algorithm. For this study dataset was taken from Kaggle. Data considers different cities of India with different dimensions like city, name, type, and significance. According to the city first find latitude and longitude for precise clustering. Future work will incorporate optimization techniques to improve cluster formation recommendation accuracy. Findings: Clustering (unsupervised learning) is a separation technique that involves assigning locations to corresponding subsets of related clusters. The weighted K-means clustering algorithm is used with the elbow method which is used for discovering the optimum number of clusters. In weighted K-means algorithm for clustering uses scaling factor wi ​which transforms the impression of individual features to the whole distance calculation. It signifies the meaning of the ith feature in the perspective of the grouping task. Offering a scaling factor permits additional tractability in modifying the outcome of specific features on the distance calculation. It enables customization of the distance metric constructed on the specific requirements and characteristics of the records and clustering task. In this study, user can select multiple dimensions of their choice and get recommendations according to their choice. The proposed weighted K-means algorithm shows a significant improvement in accuracy which considers the proportion of correct recommendations out of all recommendations. A comparison with traditional K-means was conducted, where the weighted algorithm achieved a 17% higher accuracy due to its ability to give importance to specific features. The future version of the proposed system will incorporate optimization techniques for enhanced performance. Novelty: The suggested solution in this paper demonstrates that the user can enter the city of their choice. The recommended method indicates the city and nearby predilections once the user has selected their parameters, such as consuming formations or name or type. The ratio of relevant destinations that have been successfully recommended is 18% more compared to the K-means clustering algorithm. Keywords: Recommender System, Clustering, Destination Recommender System (DRS), Machine Learning, Weighted K-means clustering Algorithm

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.

Federated Learning for Personalized Financial Recommendations

Abstract: Federated Learning (FL) has been the solution to one of those biggest contributors that have scared us from using Machine learning, i.e. Privacy [ for sensitive domains like finance ]. This survey focuses on leveraging Federated Learning in creating personalized recommendation systems for financial products such as loans, investment plans, credit cards and savings. For example, classical recommendation systems usually work in a centralized way to store a lot of sensitive financial data (or derivative structures), this setting presents other well-known privacy issues and security risks. FL provides a solution that enables multiple financial institutions to work together to train a global recommendation model without the need any of them to share their raw customer data. The paper reviews federated learning, and some key take ways are on differential privacy and secure aggregation to protect customer data while improving the quality of recommendations. We also talk about how to solve some problems like heterogeneity of the data, model convergence and regulatory constraints required in financial methods. The paper concludes by detailing future directions for federated learning in finance, with focus on how personalized financial recommendations can be dramatically transformed while preserving user privacy.

PERFECT: Personalized Exercise Recommendation Framework and architECTure

Background : The health benefits of regular physical activity (PA) are well-established and widely acknowledged. Through the integration of wearable trackers, the Internet of Things (IoT)—a network of interconnected devices capable of collecting and exchanging data—coupled with mobile health (mHealth), which refers to the use of mobile devices to support medical and public health practices, it is now feasible to systematically gather and present individual exercise behaviors. This advanced approach enables the precise correlation of users’ physiological data and daily activities with their specific fitness needs, offering a personalized pathway to improving health outcomes. Objective : This study aims to enhance PA levels among individuals by developing a personalized exercise recommendation system. Utilizing reinforcement learning, the system proposes tailored exercise plans based on biomarkers and the user’s specific context. Methods : In this study, we developed applications for smartphones and smartwatches designed to gather, monitor, and recommend exercise routines through the application of a contextual multi-arm bandit algorithm. To evaluate the efficacy of this mHealth exercise regimen, we enlisted the participation of twenty female college students. Results : The outcomes of our investigation revealed a significant enhancement in the average daily duration of exercise (P \({\lt}\) . 001). Participants expressed high levels of satisfaction with both the walking program and the recommendation system, achieving average ratings of 4.31 (SD \(=\) 0.60) and 3.69 (SD \(=\) 0.95), respectively, on a 5-point scale. Furthermore, the average scores for participants’ confidence in safely performing the recommended walking exercises, as well as their perception of the study’s effectiveness in meeting their PA needs, were both above 4, indicating a positive reception and confidence in the program’s design and implementation. Conclusions : The evolution of the IoT and wearable technology has marked the beginning of a new era for mHealth systems, particularly in the personalization of health interventions. Such advancements enable the precise personalization of PA recommendations, potentially enhancing user engagement and performance outcomes. This paper introduces a novel exercise recommendation system that utilizes reinforcement learning to personalize walking exercises based on the user’s biomarkers and context, aiming to improve the user’s aerobic capacity significantly.