User Features Research Articles

DAG-Aware Variational Autoencoder for Social Propagation Graph Generation

Propagation models in social networks are critical, with extensive applications across various fields and downstream tasks. However, existing propagation models are often oversimplified, scenario-specific, and lack real-world user social attributes. These limitations detaching from real-world analysis lead to inaccurate representations of the propagation process in social networks. To address these issues, we propose a User Features Attention-based DAG-Aware Variational Autoencoder (DAVA) for propagation graph generation. First, nearly 1 million pieces of user attributes data are collected. Then DAVA can integrate the analysis of propagation graph topology and corresponding user attributes as prior knowledge. By leveraging a lightweight attention-based framework and a sliding window mechanism based on BFS permutations weighted by user influence, DAVA significantly enhances the ability to generate realistic, large-scale propagation data, yielding graph scales ten times greater than those produced by existing SOTA methods. Every module of DAVA has flexibility and extension that allows for easy substitution to suit other generation tasks. Additionally, we provide a comprehensive evaluation of DAVA, one focus is the effectiveness of generated data in improving the performance of downstream tasks. During the generation process, we discover the Credibility Erosion Effect by modifying the generation rules, revealing a social phenomenon in social network propagation.

Security issues of news data dissemination in internet environment

With the rise of artificial intelligence and the development of social media, people's communication is more convenient and convenient. However, in the Internet environment, the untrue dissemination of news data leads to a large number of problems. Efficient and automatic detection of rumors in social platforms hence has become an important research direction in recent years. This paper leverages deep learning methods to mine the changing trend of user features related to rumor events, and designs a rumor detection model called Time Based User Feature Capture Model(TBUFCM). To obtain a new feature vector representing the user's comprehensive features under the current event, the proposed model first recomputes the user feature vector by using feature enhancement function. Then it utilizes GRU(Gate Recurrent Unit, GRU) and CNN(Convolutional Neural Networks, CNN) models to learn the global and local changes of user features, respectively. Finally, the hidden rumor features in the process of rumor propagation can be discovered by user and time information. The experimental results show that TBUFCM outperforms the baseline model, and when there are only 20 forwarded posts, it can also reach an accuracy of 92%. The proposed method can effectively solve the security problem of news data dissemination in the Internet environment.

Personalized Intelligent Recommendation Model Construction Based on Online Learning Behavior Features and CNN

The current intelligent recommendation models in online learning systems suffer from data sparsity and cold start problems. To address the data sparsity problem, a collaborative filtering recommendation algorithm model (SACM-CF) based on an automatic coding machine is proposed in the study. The model can extract the online learning behavior features of users and match these features with the learning resource features to improve the recommendation precision. For the cold-start problem, the study proposes a CBCNN model based on CNN, using the language model as the input of the model and the implicit factor as the output of the model. To avoid the problem of over-smoothing the implicit factor model, which affects the recommendation precision, an improved matrix decomposition method is proposed to constrain the output of the CNN and improve the model precision. The RMSE of SACM-CF is 0.844 and the MAE is 0.625. The MAE value of CBCNN is 0.72, the recall value is 0.65, the recommendation precision is 0.954 and the F1-score is 0.84. The metrics of SACM-CF and CBCNN are better than the existing state-of-the-art recommendation models. SACM-CF and CBCNN outperform the existing state-of-the-art intelligent recommendation models in all metrics. Therefore, the SACM-CF model and the CBCNN model can effectively improve the precision of the online learning system in recommending interesting learning resources to users, thus avoiding users' wasted learning time in searching and selecting learning resources and improving users' learning efficiency.

Dynamic user profile construction and its application to smart product-service system design: A maternity-oriented case study

The human-centric philosophy is considered a crucial development direction for smart Product-Service Systems (smart PSS). Smart PSS has effectively advanced the circular economy by enhancing user experience, incorporating servitization and digital servitization, and extending product lifecycle, among other key aspects of sustainable development. However, there is currently a lack of a reasonable, accurate, and actionable framework and methodology to express the human element, namely user profiling, within this philosophy. To address this gap, this paper utilizes statistical methods such as factor analysis, cluster analysis, and regression analysis, building upon the concept of traditional user profiling. The aim is to integrate the three prominent approaches of goal-oriented, scenario-based, and data-driven user profiling, with the goal of complementing each other and designing a top-level user profiling framework for smart PSS. Furthermore, Industry 4.0 technologies and text mining techniques are employed to collect data on users' product and service usage. As these data contain real-time information about user needs, behaviors and goals at different time periods, they can be used to construct dynamic features of user profiling, and ultimately achieve the construction of dynamic user profiling for smart PSS. To validate the proposed user profiling framework and dynamic user features of smart PSS, this paper presents a case study focusing on the user group of maternal women. This case study promotes the in-depth exploration of smart PSS research in expressing the human element.

Temporal pattern-aware QoS prediction by Biased Non-negative Tucker Factorization of tensors

Dynamic quality of service (QoS) data contain rich temporal patterns of user-service interactions, which are vital for better understanding user behaviors and service conditions. Canonical polyadic (CP)-based latent factorization model has proven to be capable of capturing such patterns. However, it models the relations among latent features of user, service and time in a rigid and unnatural way, causing its failures in capturing the complex patterns when the target QoS data become massive. To address that issue, this paper proposes a Biased Non-negative Tucker Factorization of 3D tensors (BNTucF) model with the four-fold ideas: (1) utilizing a core tensor for modeling the complex interactions among latent features; (2) incorporating linear biases into the model for accurate descriptions on QoS fluctuation; (3) constraining the model to be non-negative for describing QoS non-negativity; (4) deducing a single latent factor-dependent, multiplicative updating scheme for training the model in an efficient density-oriented way. Empirical studies demonstrate that the proposed BNTucF can learn complex dynamic user-service interaction patterns more accurately, hence achieving accurate predictions on missing QoS data.

Study of surface plasmon resonance with zirconium oxide multilayer coatings of silver and gold films: reflection polarization modes and sensitivity

We propose a surface plasmon resonance (SPR) sensor based on multilayer films with silver (Ag) and gold (Au) interfaces coated with a thin layer of zirconium oxide (ZrO2) in Kretschmann configuration. A dielectric layer of ZrO2 acts as the prism coating and the second layer is deposited after the metal film in the dielectric/metal/dielectric (DMD) system. In order to achieve the best design for the sensor, the dependence of the sensitivity on the angle of incidence and the thickness of the alternating layer is comprehensively explored. The polarization change results are shown with the angles Ψ and ∆ which are the traditional ellipsometry measurement angles of the optical properties of the DMD system. The advantage of Ag/ZrO2 multilayers over Au and Au/ZrO2 for SPR refractive index detection in terms of sensitivity, as well as the Ag/ZrO2 interface offers the possibility of studies with promising features for SPR users to generalize this type of interface. The proposed DMD sensor is expected to find application in biochemical sensing due to its resolution.

The female nomination on the basis of marital status in Russian Internet texts: functional and linguo-ecological aspects

The article is devoted to the gendered lexical unit which constitutes initial abbreviation derived from the idiom «razvedenka s pricepom» (divorsed woman with a child) and is actively used by Russian speaking Internet users to denote a female person on the basis of her marital status. The article describes the history of the appearance of the unit, its derivational and semantical characteristics, semantic and contextual connections and its functioning specifics in the Internet texts. The research is conducted on the material of the author's corpus of the contexts of use including 1 255 speech products published in between 2014 and 2023 in the communicative space of such social platforms as (425 units), vk.com (336 units), YouTube (190 units), Yandex.Dzen (173 units). The author applies the methods of quantative, semantic, conceptual, derivational, contextual and discursive analyses. The article specifies that the gendered abbreviation is derived from the linguocynical idiom, having been used in the male speech long before the spread of the Internet. The research reveals that the abbreviation use underlines the artifact conceptual metaphor generated by the initial phraseological combination. The divorced mother deanimization is regularly strengthened by the abrriviation's contextual environment. It reveals that 1 175 contexts (93,62 %) contain verbalized speaker's conception of financial aspect in sexual, romantic or family relationship caused by the actualization of consumer strategy in interpersonal ineteraction and by the ubiquitous consumeristic philosophy. It proves that the use of the innovation RSP is a feature of male Internet users' speech (982 contexts; 78,24 %) and is not typical for female speech (270 contexts; 21,51 %). In female speech the abbreviation carries out primarily the metalinguistic function and much less frequently nominative and evaluative functions. In the Internet texts produced by male persons the nomination not only promotes expressing the divorced mother with a child negative evaluation but also facilitates deconstruction, deaxiologization and desacralization of the concept «Mother». This allows us to qualify the abbreviation as a linguo-perversion which is a special kind of language trauma which represents a speech phenomenon generating the traditional symbol ambivalence and leading towards worldview destruction.

Matrix Factorization Recommendation Algorithm Based on Attention Interaction

Recommender systems are widely used in e-commerce, movies, music, social media, and other fields because of their personalized recommendation functions. The recommendation algorithm is used to capture user preferences, item characteristics, and the items that users are interested in are recommended to users. Matrix factorization is widely used in collaborative filtering algorithms because of its simplicity and efficiency. However, the simple dot-product method cannot establish a nonlinear relationship between user latent features and item latent features or make full use of their personalized information. The model of a neural network combined with an attention mechanism can effectively establish a nonlinear relationship between the potential features of users and items and improve the recommendation accuracy of the model. However, it is difficult for the general attention mechanism algorithm to solve the problem of attention interaction when the number of features between the users and items is not the same. To solve the above problems, this paper proposes an attention interaction matrix factorization (AIMF) model. The AIMF model adopts a symmetric structure using MLP calculation. This structure can simultaneously extract the nonlinear features of user latent features and item latent features, thus reducing the computation time of the model. In addition, an improved attention algorithm named slide-attention is included in the model. The algorithm uses the sliding query method to obtain the user’s attention to the latent features of the item and solves the interaction problem among different dimensions of the user, and the latent features of the item.

Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features.

Accurate short-term load forecasting (STLF) is essential for power grid systems to ensure reliability, security and cost efficiency. Thanks to advanced smart sensor technologies, time-series data related to power load can be captured for STLF. Recent research shows that deep neural networks (DNNs) are capable of achieving accurate STLP since they are effective in predicting nonlinear and complicated time-series data. To perform STLP, existing DNNs use time-varying dynamics of either past load consumption or past power correlated features such as weather, meteorology or date. However, the existing DNN approaches do not use the time-invariant features of users, such as building spaces, ages, isolation material, number of building floors or building purposes, to enhance STLF. In fact, those time-invariant features are correlated to user load consumption. Integrating time-invariant features enhances STLF. In this paper, a fuzzy clustering-based DNN is proposed by using both time-varying and time-invariant features to perform STLF. The fuzzy clustering first groups users with similar time-invariant behaviours. DNN models are then developed using past time-varying features. Since the time-invariant features have already been learned by the fuzzy clustering, the DNN model does not need to learn the time-invariant features; therefore, a simpler DNN model can be generated. In addition, the DNN model only learns the time-varying features of users in the same cluster; a more effective learning can be performed by the DNN and more accurate predictions can be achieved. The performance of the proposed fuzzy clustering-based DNN is evaluated by performing STLF, where both time-varying features and time-invariant features are included. Experimental results show that the proposed fuzzy clustering-based DNN outperforms the commonly used long short-term memory networks and convolution neural networks.

Hybrid Prompt Recommendation Explanation Generation combined with Graph Encoder

Recommendation systems have been effectively utilized in various fields, but their internal decision-making methods are still largely unknown. This opaque decision-making method can greatly affect users’ trust in the recommendation system. Therefore, finding a way to explain the reasons for model decisions has become an urgent task. Previous studies often used LSTM and other models to generate recommendation explanations and explain the reasons for recommendations in text form. However, traditional methods cannot effectively use the ID information of users and items, and the text generated is highly repetitive. To solve this problem, this paper uses the method of prompt learning combined with a graph encoder to design a recommendation explanation generation model. In order to narrow the semantic gap between the ID information of users and items and natural language and capture high-level interaction information, this paper designs a graph encoder based on user similarity to learn the interactive semantic information of user and item IDs, and to construct a continuous prompt. Then, the discrete prompt composed of discrete features of users and items is combined with the continuous prompt to construct a hybrid prompt to input into the pre-trained model to generate the recommended explanation. This paper experiments on three publicly available datasets and compares them with several state-of-the-art methods to demonstrate the personalization and text quality of the generated explanations.

Improvement of the recommendation system based on the multi-armed bandit algorithm

In order to effectively solve common problems of the recommendation system, such as the cold start problem and dynamic data modeling problem, the multi-armed bandit (MAB) algorithm, the collaborative filtering (CF) algorithm, and the user information feedback are applied by researchers to update the recommendation model online and in time. In other words, the cold start problem of the recommendation system is transformed into an issue of exploration and utilization. The MAB algorithm is used, user features are introduced as content, and the synergy between users is further considered. In this paper, the author studies the improvement of the recommendation system based on the multi-armed bandit algorithm. The Liner Upper Confidence Bound (LinUCB), Collaborative Filtering Bandits (COFIBA), and Context-Aware clustering of Bandits (CAB) algorithms are analyzed. It is found that the MAB algorithm can get a good maximum total revenue regardless of the content value after going through the cold start stage. In the case of a particularly large amount of content, the CAB algorithm achieves the greatest effect.

Integration of Deep Reinforcement Learning with Collaborative Filtering for Movie Recommendation Systems

In the era of big data, effective recommendation systems are essential for providing users with personalized content and reducing search time on online platforms. Traditional collaborative filtering (CF) methods face challenges like data sparsity and the new-user or cold-start issue, primarily due to their reliance on limited user–item interactions. This paper proposes an innovative movie recommendation system that integrates deep reinforcement learning (DRL) with CF, employing the actor–critic method and the Deep Deterministic Policy Gradient (DDPG) algorithm. This integration enhances the system’s ability to navigate the recommendation space effectively, especially for new users with less interaction data. The system utilizes DRL for making initial recommendations to new users and to generate optimal recommendation as more data becomes available. Additionally, singular value decomposition (SVD) is used for matrix factorization in CF, improving the extraction of detailed embeddings that capture the latent features of users and movies. This approach significantly increases recommendation precision and personalization. Our model’s performance is evaluated using the MovieLens dataset with metrics like Precision, Recall, and F1 Score and demonstrates its effectiveness compared with existing recommendation benchmarks, particularly in addressing sparsity and new-user challenges. Several benchmarks of existing recommendation models are selected for the purpose of model comparison.

Joint inter-word and inter-sentence multi-relation modeling for summary-based recommender system

Review is an essential piece of information that influences users’ decisions, but excessively long reviews not only degrade the user experience but also affect the accuracy of the recommender system. Therefore, Joint Inter-Word and Inter-Sentence Multi-Relation Modeling for the Summary-based Recommender System (MRSR) is proposed in this paper. In MRSR, the concise summary information serves as representation data, and a multi-relation modeling module is designed to construct user and item characteristics from two levels. Specifically, the inter-word relation modeling module, which consists of the Transformer and the pooling layer, is used to learn the long dependencies of summaries and extract word-level features by calculating the relative weights between words within sentences. The inter-sentence relation modeling module is used to enrich the sentence-level features of users and items, where an attention mechanism is employed to perceive the relative weights between different summary sentences. Finally, the fusion layer based on multi-head attention and the prediction layer based on attentional factorization machine are implemented to conduct the shallow and deep interactions between user and item features, based on which MRSR completes the final rating prediction. Extensive experimental results on five publicly available datasets demonstrate that MRSR achieves a 5.94% improvement in RMSE metrics compared to state-of-the-art methods. Furthermore, the accuracy of most existing models is improved by 1%∼2% while the inference time is reduced by 10% by utilizing summaries as representation data. It proves the efficiency and effectiveness of our proposed approach, which has promising application prospects.

The Effect of Feature Selection on the Accuracy of X-Platform User Credibility Detection with Supervised Machine Learning

In the era of digital information, online platforms play a crucial role in shaping public opinion. However, the extensive spread of misinformation and fake news poses a significant challenge, largely fueled by non-credible users. Detecting user credibility is vital for ensuring the reliability of information on these platforms. This study employs supervised machine learning algorithms, leveraging key user features to enhance credibility detection. Feature selection methods, specifically SelectKBest and correlation-based algorithms, are explored for their impact on X-Platform user credibility detection. Utilizing various classifiers, including support vector machine, logistic regression, and XGBoost, experiments are conducted on the ArPFN dataset, which is a labeled, balanced, publicly available dataset. The evaluation includes measures like accuracy, precision, recall, and F1-score to assess efficiency. This research considers feature categories and selection methods with SML to detect their impact on the accuracy of X-Platform user credibility detection, making this research a reference for researchers and practitioners working in the field of SML, feature engineering, and social media analysis. We aim to advance the field’s understanding of effective strategies for mitigating the spread of fake news. The novelty of this study lies in the comprehensive exploration of feature selection methods and their influence on credibility detection, contributing valuable insights for future research in this domain.

Enable Excel-Based Basic Cybersecurity Features for End Users by Using Python-Excel Integration

Enable Excel-Based Basic Cybersecurity Features for End Users by Using Python-Excel Integration

Dynamic and Static Representation Learning Network for Recommendation.

Existing review-based recommendation methods learn a latent representation of user and item from user-generated reviews by a static strategy, which are unable to capture the dynamic evolution of users' interests and the dynamic attraction of items. Here, we propose a dynamic and static representation learning network (DSRLN) to improve the rating prediction accuracy by exploring fine-grained representations of users and items. Specifically, we built DSRLN with a dynamic representation extractor to model the dynamic evolution of users' interests by exploring the inner relations of an interaction sequence, and with a static representation extractor to model the users' intrinsic preferences by learning the semantic coherence and feature strength information from reviews. To identify the different influences of dynamic and static features for different users, a personalized adaptive fusion module was designed using a weighted attention mechanism. Extensive experiments on five real-world datasets from Amazon demonstrated the superiority of the proposed model, and the additional ablation studies verified the effectiveness of the components designed in the DSRLN model.

F3l: an automated and secure function-level low-overhead labeled encrypted traffic dataset construction method for IM in Android

Fine-grained function-level encrypted traffic classification is an essential approach to maintaining network security. Machine learning and deep learning have become mainstream methods to analyze traffic, and labeled dataset construction is the basis. Android occupies a huge share of the mobile operating system market. Instant Messaging (IM) applications are important tools for people communication. But such applications have complex functions which frequently switched, so it is difficult to obtain function-level labels. The existing function-level public datasets in Android are rare and noisy, leading to research stagnation. Most labeled samples are collected with WLAN devices, which cannot exclude the operating system background traffic. At the same time, other datasets need to obtain root permission or use scripts to simulate user behavior. These collecting methods either destroy the security of the mobile device or ignore the real operation features of users with coarse-grained. Previous work (Chen et al. in Appl Sci 12(22):11731, 2022) proposed a one-stop automated encrypted traffic labeled sample collection, construction, and correlation system, A3C, running at the application-level in Android. This paper analyzes the display characteristics of IM and proposes a function-level low-overhead labeled encrypted traffic datasets construction method for Android, F3L. The supplementary method to A3C monitors UI controls and layouts of the Android system in the foreground. It selects the feature fields of attributes of them for different in-app functions to build an in-app function label matching library for target applications and in-app functions. The deviation of timestamp between function invocation and label identification completion is calibrated to cut traffic samples and map them to corresponding labels. Experiments show that the method can match the correct label within 3 s after the user operation.

Research on personalized recommendation of teaching resources based on joint probability matrix decomposition model and CNN improvement algorithm

Abstract This study proposes a teaching resource recommendation method (TRRDLMF) based on deep learning and probabilistic matrix decomposition, aiming to improve teaching resource recommendation’s accuracy and personalization level. The study combines hybrid neural network feature extraction of teaching resources and user feature extraction based on extended noise-reducing self-encoder and extended probabilistic matrix decomposition-based teaching resource recommendation technique. The study validates the model’s effectiveness by conducting empirical analyses on four online learning platform datasets of different sizes. The recommendation system can effectively track learners’ knowledge mastery status and learning preferences, and provide corresponding teaching resources. Test question similarity and difficulty analysis results show that the model can accurately capture the correlation between test questions and provide complementary data in education. The personalized recommendation analysis reveals the learners’ knowledge preference states that change with learning, demonstrating the model’s ability to give explanations at the level of learners’ knowledge preferences. The analysis of the impact of data size on recommendation results shows that the recommender system can achieve up to 73.12% accuracy and 97.89% recall for different TOP-K recommendation lists, with the best F-value at TOP-6. The personalized recommendation system for teaching resources based on the joint probability matrix decomposition model and CNN improvement algorithm proposed by the research demonstrates significant effects in improving the accuracy and personalization level of teaching resources recommendation, which helps learners to acquire suitable learning resources more efficiently, and has high practical value and promotion potential.

Library User Feature Data Generation Based on Genetic Algorithm

Library User Feature Data Generation Based on Genetic Algorithm

Predicting Purchasing Behavior on E-Commerce Platforms: A Regression Model Approach for Understanding User Features that Lead to Purchasing

Predicting Purchasing Behavior on E-Commerce Platforms: A Regression Model Approach for Understanding User Features that Lead to Purchasing