User Behavior Prediction Research Articles

Movie Recommendation System Based on Graph Neural Network and Contextual Information

With the rapid growth of users, traditional collaborative filtering methods continue to struggle with handling data sparsity and cold start issues, leading to significantly reduced recommendation accuracy. To address these persistent challenges, this study proposes a movie recommendation system that integrates Graph Neural Networks (GNNs) with temporal contextual information. GNNs model user-movie interactions as graph structures, with users and movies represented as nodes and their interactions as edges. By incorporating temporal context, the model captures dynamic user preferences that evolve over time, allowing for more personalized and context-aware recommendations. The GNN model achieved an RMSE of 1.51, which further improved to 1.45 with the inclusion of temporal context, demonstrating the crucial role of contextual information in enhancing the system’s ability to predict user behavior. These findings highlight the substantial potential of integrating GNN with contextual data to significantly improve the overall performance of recommendation systems, especially in scenarios characterized by sparse data or limited user-item interactions.

User Behavior Analysis and Prediction Based on Differential Evolution Algorithm Optimized Transformer Combined with Bidirectional Long Short-Term Memory Neural Network

This paper discusses a method combining differential evolution algorithm and bidirectional long short-term memory neural network (BiLSTM) to optimize Transformer model, aiming to improve the accuracy of bank customer credit analysis and prediction. By optimizing the parameters of Transformer model through differential evolution algorithm and combining with the powerful time series analysis capability of BiLSTM, an efficient credit prediction model is constructed. In the process of model training, with the increase of the number of iterations, the correct rate of the model steadily improves and eventually becomes stable. Meanwhile, the value of the loss function also gradually decreases, indicating that the performance of the model on the training data is gradually enhanced. In addition, through confusion matrix analysis, we found that the prediction accuracy of the training set reached 86.15%, and that of the test set reached 82.91%, which showed that the model not only performed well on the training data, but also had strong generalization ability on the unseen data. In addition, the F1 score and DAUC values of the model are both positive, which further confirms the superiority of the model's prediction effect. The research results of this paper show that the Transformer model optimized by differential evolution algorithm and BiLSTM has high accuracy and good generalization ability in the analysis and prediction of bank customer credit. This optimization method not only improves the prediction performance of the model, but also enhances the stability and reliability of the model in practical application by reducing overfitting. Through this optimization strategy, banks can more accurately assess the credit risk of customers and make more informed credit decisions. This research provides a new technical means for credit risk assessment in the financial field, which is helpful to improve the efficiency and security of banking business.

Social Media User Behavior Prediction Based on VONN and MRA

Social Media User Behavior Prediction Based on VONN and MRA

AI-Driven Energy Efficiency Optimizations in mHealth Applications: A Comprehensive Review on User Behavior Prediction and System Performance

Recently, mHealth applications have gained immense popularity, revolutionizing healthcare management for chronic diseases and fitness tracking. However, continuous data processing and transmission increase the strain on battery life. This study examines AI and machine learning-based techniques to reduce energy consumption in mHealth applications without compromising functionality. Adaptive sampling, task scheduling, and predictive user behavior modeling were implemented, significantly reducing power consumption and extending battery life. Challenges such as data privacy and model generalization in deploying these AI technologies are also addressed, along with future research and broader adoption.

Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework

This study presents an approach that uses session and page view data collected through the CAWAL framework, enriched through specialized processes, for advanced predictive modeling and anomaly detection in web usage mining (WUM) applications. Traditional WUM methods often rely on web server logs, which limit data diversity and quality. Integrating application logs with web analytics, the CAWAL framework creates comprehensive session and page view datasets, providing a more detailed view of user interactions and effectively addressing these limitations. This integration enhances data diversity and quality while eliminating the preprocessing stage required in conventional WUM, leading to greater process efficiency. The enriched datasets, created by cross-integrating session and page view data, were applied to advanced machine learning models, such as Gradient Boosting and Random Forest, which are known for their effectiveness in capturing complex patterns and modeling non-linear relationships. These models achieved over 92% accuracy in predicting user behavior and significantly improved anomaly detection capabilities. The results show that this approach offers detailed insights into user behavior and system performance metrics, making it a reliable solution for improving large-scale web portals’ efficiency, reliability, and scalability.

RAG-based explainable prediction of road users behaviors for automated driving using knowledge graphs and large language models

The prediction of road user behaviors in the context of autonomous driving has attracted considerable attention from the scientific community in recent years. Most works focus on predicting behaviors based on kinematic information alone, a simplification of reality since road users are humans, and as such they are highly influenced by their surrounding context. In addition, a large plethora of research works rely on powerful Deep Learning techniques, which exhibit high-performance metrics in prediction tasks but may lack the ability to fully understand and exploit the contextual semantic information contained in the road scene, not to mention their inability to provide explainable predictions that can be understood by humans. In this work, we propose an explainable road users’ behavior prediction system that integrates the reasoning abilities of Knowledge Graphs (KG) and the expressiveness capabilities of Large Language Models (LLM) by using Retrieval Augmented Generation (RAG) techniques. For that purpose, Knowledge Graph Embeddings (KGE) and Bayesian inference are combined to allow the deployment of a fully inductive reasoning system that enables the issuing of predictions that rely on legacy information contained in the graph, as well as on current evidence gathered in real-time by onboard sensors. Two use cases have been implemented following the proposed approach: 1) Prediction of pedestrians’ crossing actions; and 2) Prediction of lane change maneuvers. In both cases, the performance attained exceeds the current state-of-the-art in terms of anticipation and F1 score, showing a promising avenue for future research in this field.

Deep Learning-Based Framework for Social Media User Behavior Analysis and Prediction

Deep Learning-Based Framework for Social Media User Behavior Analysis and Prediction

Designing User Experience Improvement and User Behavior Pattern Recognition Algorithms in Design Operation

Enhancing user experience (UX) is a key component in customer retention and sales promotion in e-commerce platforms. To build an effective UX model it is necessary to predict the user behavior more accurately and develop UX model that is tailored based on those behavior patterns. Existing models lack the ability to integrate advanced Machine Learning (ML) models to address the challenges. This study is an attempt to tackle these limitations that employs advanced AI tools to predict user behavior so that to construct an more effective UX model. The study involved 80 users from China who were aged 26 to 52, with diverse backgrounds in education, occupation, and tech proficiency. The work have employed Google Analytics, Hotjar, and FullStory to collect the user interactions and by using Generalized Sequential Pattern (GSP) algorithm, Decision Trees (DT), and Logistic Regression (LR) the work attempts to accurately predict the user behavior patterns. The results show that the model achieved better accuracy of 0.8795 and an F1 Score of 0.8610 on the test dataset. It also excelled in conversion rate (12.34%) and bounce rate (28.65%) which show effectiveness in retaining users and converting visits into actions.

OPTIMIZING PROGRAMMATIC ADVERTISING: A MACHINE LEARNING APPROACH TO PREDICTIVE AD TARGETING

In the ever-evolving landscape of digital advertising, programmatic advertising has emerged as a pivotal tool for automating ad placement and targeting audiences at scale. However, traditional methods often fall short in accurately predicting user behavior and delivering relevant ads to the right audiences. This study explores the potential of machine learning (ML) techniques to enhance predictive ad targeting within the programmatic advertising ecosystem. By applying a range of supervised and unsupervised ML models, including decision trees, neural networks, and clustering algorithms, we assess the ability of these models to improve ad relevance and engagement while optimizing budget allocation. Our findings reveal that ML-driven predictive targeting significantly increases click-through rates (CTR) and conversion rates compared to conventional targeting strategies. This research highlights the implications of using ML to improve ad targeting precision, reduce ad spend wastage, and enhance user experience. These insights contribute to the advancement of programmatic advertising strategies by demonstrating the transformative impact of AI and ML on audience segmentation and predictive ad delivery.

Research on the Implementation of Financial Risk Control Models and Algorithms Based on Machine Learning

Nowadays, machine learning has been rapidly applied to the financial services industry. With the applications of machine learning, it will bring challenges to the stable development of the financial industry. This paper studies the user behavior analysis algorithm based on operator big data, and provides financial risk control mechanism for financial enterprises. Taking finance as the data collection object, in the user behavior prediction algorithm, the experiment uses the spectral clustering algorithm to cluster the non-convex data samples, and converts the samples to the Laplacian feature space. It not only reduces the dimension, but also clusters the data, and obtains the global optimal solution. The proposed algorithm is analyzed experimentally, and the tool prototype is realized. The developed gadgets have also been applied in enterprises. The research provides a method for the construction of a risk control model in the financial industry, thereby helping financial enterprises to better control the risk of lending. This paper also provides reference for the performance improvement of the model.

End-to-End Modeling and Long Short-Term Memory Application in Time Series Modeling

With the vigorous development of e-commerce, accurately modeling and predicting user behavior has become a key factor in improving business efficiency. Precisely understanding user behavior not only enables companies to provide personalized services but also allows them to stand out in the intense market competition. This study aims to explore the effectiveness of applying end-to-end models, Long Short-Term Memory (LSTM), and attention mechanisms in time series modeling to enhance the performance of modeling and predicting user behavior in e-commerce. In the methodology section, we first introduce the basic principles of the end-to-end model, which extracts features directly from raw data for prediction, avoiding the need for intricate feature engineering. Simultaneously, we introduce Long Short-Term Memory (LSTM) to better capture long-term dependencies in time series data.

Algorithmic Intrusion: The Erosion of Personal Privacy in Digital Age

In the era of global electronicization, the digital transformation of communication technology has led to the widespread collection and storage of personal information in the databases of online services, raising concerns about personal privacy. Companies use algorithms to collect and analyze this data and effectively predict user behavior to optimize services and drive profit growth. However, with the rapid development of the data economy and the increase in the scale of data collection, it has become increasingly difficult for the media to manage privacy, the boundaries of personal privacy have gradually blurred, and the public's concerns about privacy have also increased. This paper will take algorithms, the primary driving force of the data economy, as the starting point to analyze how algorithms invade personal privacy through recommender systems, advertising target, and social media functions, and how they use this private data to influence users' perceptions and behaviors.

Advertising in digital applications and mobile games as a monetization element

The paper explores the specifics of the term "monetization" for video games and examines similar monetization systems for non-gaming platforms. It reviews the dynamics of the video game market as a whole, particularly the mobile game market, and analyzes the current state of the mobile gaming market. The stages of market formation and the evolution of digital goods are presented in chronological order, using video games as an example, along with the evolution of advertising integrations from the 1990s to the present day. Examples of advertising usage before the widespread adoption of mobile devices and connectivity are provided, such as product placement and DLC. The characteristic features of different sales systems are defined: Free-to-play, Freemium, Pay-to-play. Their advantages and distinctive monetization features are outlined, and the reasons for the dominance of Free-to-play and Freemium in the current mobile gaming market are identified. The work offers a detailed characterization of the main types of video advertising in mobile games: native advertising, video advertising, rewarded video ads, demos, and contextual advertising. The reasons for the proliferation of each type of advertising are revealed, and the main advantages and challenges of using video advertising, in general, are identified for both advertisers and publishers. Examples from modern mobile games and applications are systematized depending on the advertiser, the game distribution model, and the complexity of implementation. Specialized monetization elements specific to certain types of video advertising are examined. The research results can be used to build a monetization strategy for developing freemium and free-to-play gaming or entertainment projects, to assess the appropriateness of using video games as a platform for placing one's advertising, or to predict user behavior from the perspective of the experience economy and interaction with digital goods.

Application and Optimization of Various Machine Learning Models in Social E-Commerce Marketing Strategies

In the context of rapid development in social e-commerce, the optimization of marketing strategies urgently requires new technological approaches. This study investigates the application of four artificial intelligence algorithms—supervised learning, deep learning, unsupervised learning, and reinforcement learning—in Douyin live shopping and Kuaishou platform shopping, proposing a series of innovative marketing strategies. Based on an analysis of 920,000 user behavior records, we evaluate the effectiveness of each algorithm in user behavior prediction, personalized recommendation, advertisement placement optimization, and customer segmentation. The results indicate that the deep learning model achieved a prediction accuracy of 94.8%, enhancing user satisfaction by 19.7%. The supervised learning model achieved a classification accuracy of 89.3%. The reinforcement learning model increased advertisement click-through rates by 24.6%. The unsupervised learning model excelled in customer segmentation. By utilizing hybrid models and improved algorithms, marketing effectiveness was further enhanced, providing new directions and strategies for marketing practices in the social e-commerce sector.

Where scrollbars are clicked, and why

Scrolling is a widely used mean to interact with visual displays, usually to move content to a certain target location on the display. Understanding how user scroll might identify potentially suboptimal use and allows to infer users’ intentions. In the present study, we examined where users click on a scrollbar depending on the intended scrolling action. In two online experiments, click positions were systematically adapted to the intended scrolling action. Click position selection could not be explained as strict optimization of the distance traveled with the cursor, memory load, or motor-cognitive factors. By contrast, for identical scrolling actions click positions strongly depended on the context and on previous scrolls. The behavior of our participants closely resembled behavior observed for manipulation of other physical devices and suggested a simple heuristic of movement planning. The results have implications for modeling human–computer interaction and may contribute to predicting user behavior.

A prediction model for rumor user propagation behavior based on sparse representation and transfer learning

This paper introduces a prediction model rooted in sparse representation and transfer learning, with the primary objective of predicting user behavior during rumor propagation. Users' behavior is dynamic, and rumor, rumor-refuting, and rumor-promoting messages interact dynamically, according to the model. Firstly, this paper proposes to compensate for the low performance of propagation prediction models due to data sparsity at the beginning of rumor topics' lifes. Transfer learning is used to compensate for the data sparsity problem at the beginning of the topic. To map the rumor topic space effectively, a low-rank dense vectorization algorithm based on sparse representation is proposed. Finally, to mine the potential impact of multiple types of information on users, a model based on three-party game theory is constructed. It considers the complex interaction between rumor, rumor-refuting, and rumor-promoting information in the propagation of rumor. Additionally, this paper develops a model of dynamic user behavior prediction using Rumor-Attention-Mechanism-Graph-Attention (RAM-GAT) to predict rumor propagation. Experiments demonstrate that our model can effectively mine the interaction influence between multiple types of information. It can accurately predict user behavior when initial data is insufficient. In addition, rumor propagation patterns and trends are revealed.

Big Data Analysis and User Behavior Prediction of Social Networks Based on Artificial Neural Network

The prediction of user behavior in social networks is of great significance for understanding user dynamics, personalized recommendation, and information dissemination. With the development of artificial intelligence (AI) technology, especially the application of artificial neural networks in big data analysis, new solutions and technical means have been provided for the analysis of user behavior in social networks. This study constructs a social network user behavior prediction model based on artificial neural networks. The article first reviews related research, establishes a research framework, and then describes in detail the functional structure of the user behavior prediction system. The key data structure design is meticulously constructed and the model is built through steps such as data preprocessing module, 3D feature frame construction module, feature mapping module, and feature prediction module. In addition, the article also constructs a group behavior theme probability prediction model based on an improved encoder-decoder model, which further enhances the understanding of group behavior of users in social networks. Through comparative experiments, the model proposed in this paper demonstrates its effectiveness in predicting individual and group user behaviors. The experimental results show that the model can accurately predict the behavior patterns of users in social networks and is superior to existing methods in terms of prediction accuracy and computational efficiency. This research shows that artificial neural networks are a powerful tool for analyzing big data in social networks and for predicting user behavior. The success of the study verifies the effectiveness of the model, provides a new technical path for future analysis of user behavior in social networks, and is expected to be widely applied in areas such as personalized recommendation and information dissemination analysis.

Machine learning algorithms in the development of recommender systems

With the continuous development and widespread use of the Internet, individuals often find themselves overwhelmed by the vast amount of information available. To address this issue and better cater to users' personalized needs, recommender systems have emerged. A recommender system is a technology that provides users with customized content or services based on their preferences and interests. Its primary goal is to predict user behavior as accurately as possible in order to recommend relevant items. Machine learning algorithms play a crucial role in the functioning of recommender systems. These algorithms not only automatically analyze and process large volumes of data but also extract valuable features from historical data, enabling accurate predictions for unknown data. The rapid progress of deep learning technology has significantly enhanced the accuracy and efficiency of recommender systems. Deep learning enables recommender systems to better grasp user behavior, interests, and preferences, leading to more precise predictions of user needs and behaviors. Furthermore, deep learning can handle unstructured data such as images, audio, and text, extracting valuable features to improve recommendations. In the future, as machine learning and artificial intelligence technologies continue to advance and gain popularity, recommender systems will find broader applications. Apart from the traditional e-commerce sector, recommender systems can also be applied to social networks, news media, online education, and other fields. Simultaneously, in the research and implementation of recommender systems, due attention must be given to user privacy protection and data security. Only by ensuring user privacy and data security can recommender systems effectively meet user needs and gain wider acceptance.

User Interaction Interface Design and Innovation Based on Artificial Intelligence Technology

At a time when artificial intelligence is widely used in all walks of life, the way users interact with the digital world also needs to incorporate intelligent elements to reduce the cost of connectivity. This cost can be quantified through "experience metrics", which reveal the problems users encounter when using the interface (UI), and make targeted optimization. With AI, deep learning and prediction of user behavior can be achieved to anticipate and address potential barriers to use in UI design. This will not only improve the user experience, but also promote the development of UI design in a more user-friendly and intelligent direction. Through accurate analysis of experience indicators and combined with AI technology to optimize design, the gap between users and the digital world can be greatly reduced, making digital products more suitable for user needs and achieving seamless interactive experience.

A Robust Sequential Recommendation Model Based on Multiple Feedback Behavior Denoising and Trusted Neighbors

At present, most of the personalized sequential recommendations utilize users’ implicit positive feedback (such as clicks) to predict user behavior, ignoring the impact of implicit negative feedback and explicit feedback on the accuracy of recommendation results prediction. In this paper, we propose a robust sequence recommendation model based on multi feedback behavior denoising and trusted neighbors, which utilizes multiple feedback behavior data for feature denoising and considers trusted nearest neighbor information to improve model performance. Firstly, by learning the feature representations and interactions of various types of feedback, explicit feedback is used to map and purify implicit feedback with the same and different attributes, resulting in unbiased user performance. Then, we design a filter attention network to identify highly trusted neighbor information. Finally, we integrate pure user interest representations and trusted nearest neighbor representations to improve the accuracy and robustness of the model. The experimental results on two publicly available datasets show that the proposed sequential recommendation model can achieve superior results to baseline methods in both AUC and RelaImpr.