User Profile Model Research Articles

Price discrimination in mobile gacha games: A comprehensive study of the scarcity–pricing, gachaing–user profiling, and recharging–profiting models

AbstractPrice discrimination (PD) in mobile gacha games differs from that in e‐commerce platforms due to its reliance on the concealed “probability” mechanism, which appears transparent but is actually controlled, making it harder to detect and regulate by society. To confirm the existence of PD in mobile gacha games, a comprehensive analysis of its operational modes was conducted through extensive participatory observation and in‐depth interviews with over 30 domestic gamers, game developers, and data analysis website founders. The study revealed that, contrary to academic definitions, PD does indeed occur in mobile gacha games, characterized by three interconnected models: the scarcity–pricing model, gachaing–user profiling model, and recharging–profiting model. By examining the interrelated models that comprise the PD process, concentrating on China as a special context, and offering insightful information on the effects of PD on consumer behavior and economic platforms, this study makes a noteworthy and novel contribution to the understanding of PD within the realm of mobile gacha games. The research examines the influence of PD on consumer behavior and economic decision‐making processes. Furthermore, the research discusses the difficulties of controlling PD in mobile gacha games, particularly due to the opacity of the “probability” mechanism. In conclusion, recommendations aimed at addressing the issue of PD within the realm of mobile gacha gaming in China are presented, drawing upon Lawrence Lessig's framework on “Four modalities of restraining behavior in cyberspace.”

Multi-layer and multi-source features stacking ensemble learning for user profile

Electricity bill-sensitive user profiling in the power industry is gradually being recognized as a research hotspot. A multi-layer feature construction method has been proposed, separating the mining of textual and numerical information, addressing the insufficient exploration of textual data in existing user profile processing methods. The complexity of electricity user profiles is addressed through the introduction of a two-stage predictive model based on Stacking ensemble learning. In the first stage, user sensitivity is predicted by utilizing the advantages of MLP (Multi-Layer Perceptron), CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory), and XGB (Extreme Gradient Boosting) in global, local, and missing value handling, respectively. In the second stage, electricity-sensitive users are identified by employing RF (Random Forest). The experimental results show that the MLS-SEL user profile model is higher than the models MVEM, SG and SMUPM in terms of both F1 value and accuracy rate. It is implied that users who could be more sensitive to fluctuations in electricity costs have been identified more accurately.

Quality Innovation Cultivation Strategy for College Students in the Internet Era Oriented on the Cultivation of Innovative Ability

Abstract Addressing the imperative of cultivating innovation and learning quality in 21st-century college education, this research proposes a strategic CTDE model to enhance college students’ innovative capacities. By implementing a personalized information mining technique, we have established a sophisticated user profile model that strategically distributes learning resources geared towards innovation. The methodology integrates factor analysis with K-means clustering for a customized learning experience. A targeted survey reveals notable achievements: a standout innovation ability score of 3.92 for an individual student and a class-wide enhancement, with improvements ranging from 14% to 21% after detailed work evaluations. These findings affirm the CTDE model’s efficacy in fostering student innovation and quality learning.

Analysis of Mobile Device Dual Tasking on the Move: Normal Cognitive Decline of Aging as Ground Truth for Mild Cognitive Impairment

The widespread use of mobile phones in daily life makes them a fundamental tool for the study of human behavior. In particular, they can be used as a source of additional information to help to diagnose diseases. This work is based on contrasted dual-tasking tests where cognitive performance is studied by performing tasks of high cognitive load while walking. In this case, we study significant differences in mobile device use among groups of people of different ages and examine whether they are more characteristic when the interaction takes place on the move. A study is conducted by monitoring the interaction with the mobile device for one consecutive week and analyzing the correlations between these interactions and the participants’ ages. Additionally, a user profiling model is designed to help to use this ground truth in future works focused on the early diagnosis of cognitive deficits. The results obtained contribute to preliminarily characterizing how age-related normotypical cognitive decline affects interactions with mobile devices. In addition, the pilot study generates a dataset with monitored events and interactions of 45 users that includes more than 4.5 million records.

Survey and Tutorial on Hybrid Human-Artificial Intelligence

The growing computing power, easy acquisition of large-scale data, and constantly improved algorithms have led to a new wave of artificial intelligence (AI) applications, which change the ways we live, manufacture, and do business. Along with this development, a rising concern is the relationship between AI and human intelligence, namely, whether AI systems may one day overtake, manipulate, or replace humans. In this paper, we introduce a novel concept named hybrid human-artificial intelligence (H-AI), which fuses human abilities and AI capabilities into a unified entity. It presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective control. We scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward H-AI. We then examine the key underpinning techniques of H-AI, such as user profile modeling, cognitive computing, and human-in-the-loop machine learning. Afterward, we discuss H-Al's potential applications in the area of smart homes, intelligent medicine, smart transportation, and smart manufacturing. Finally, we conduct a critical analysis of current challenges and open gaps in H-AI, upon which we elaborate on future research issues and directions.

Modeling of user profiles of financial products and comparison of purchase prediction models: Based on machine learning

In this paperwork, the Apriori algorithm can be used to describe the association between data in a database. This paper will use the demographic and financial information of the users in the data set to analyze the user profile as the basic information. This work will fit the model by optimizing the Apriori algorithm, generating a combination of two or more associated variables, and then using the decision tree to learn and output the final model. The main contribution of this paper is to explore the optimization of Apriori and the ef-fect of Apriori's combined model with the decision tree in predicting user buying behavior. Therefore, the conclusion can be drawn that in the sale of deposit products, if the custom-er has the above characteristics, such as no credit fault, no loan, housing loan, with a col-lege degree, marital status is single, and act as an administrator. The people mentioned above may be more interested in the product. In other words, it is more likely to sell suc-cessfully. In future work, in addition to deposit products, more customer buying infor-mation is highly needed to extend predictable results to broader anger.

Finding Experts in Community Question Answering System Using Trie String Matching Algorithm with Domain Knowledge

Different community question-answering systems have been evolving as online fora for knowledge sharing among users across the world. However, finding experts on the questions is a key issue in the community question-answering system. To address this issue, a novel approach is proposed in this paper for finding experts from the community question-answering website using an exact string-matching algorithm along with domain knowledge. The proposed system is designed with three phases: i) User Profile Modeling, ii) Question Preprocessing, and iii) Expert Recommendation. Initially, the matrix factorization model is adopted for user profile modeling where the tags and the answerer’s information are represented in the form of a matrix. Then, the domain-wise grouping of tags is done to minimize the search time of the tags. The question preprocessing is done using a keyword extraction algorithm to extract the keywords. Finally, the expert recommendation is accomplished using the trie string matching algorithm and key-value mapping process. For doing experiments, a stack overflow community question-answering website is utilized in this work. The performance of the system is measured and the results section proved that the system achieved 91.2% accuracy.

A Cross-Platform Personalized Recommender System for Connecting E-Commerce and Social Network

In this paper, we build a recommender system for a new study area: social commerce, which combines rich information about social network users and products on an e-commerce platform. The idea behind this recommender system is that a social network contains abundant information about its users which could be exploited to create profiles of the users. For social commerce, the quality of the profiles of potential consumers determines whether the recommender system is a success or a failure. In our work, not only the user’s textual information but also the tags and the relationships between users have been considered in the process of building user profiling model. A topic model has been adopted in our system, and a feedback mechanism also been design in this paper. Then, we apply a collative filtering method and a clustering algorithm in order to obtain a high recommendation accuracy. We do an empirical analysis based on real data collected on a social network and an e-commerce platform. We find that the social network has an impact on e-commerce, so social commerce could be realized. Simulations show that our topic model has a better performance in topic finding, meaning that our profile-building model is suitable for a social commerce recommender system.

User profiling and role evaluation of government microbloggers in the context of public emergencies

PurposeThis study aims to profile the government microbloggers and evaluate their roles. The results can help improve the governments' response capability to public emergencies.Design/methodology/approachThis study proposes the user profiling and role evaluation model of government microbloggers in the context of public emergencies. The indicators are designed from the four dimensions of time, content, scale and influence, and the feature labels are identified. Three different public emergencies were investigated, including the West Africa Ebola outbreak, the Middle East respiratory syndrome outbreak and the Shandong vaccine case in China.FindingsThe results found that most government microbloggers were follower responders, short-term participants, originators, occasional participants and low influencers. The role distribution of government microbloggers was highly concentrated. However, in terms of individual profiles, the role of a government microblogger varied with events.Social implicationsThe findings can provide a reference for the performance assessment of the government microbloggers in the context of public emergencies and help them improve their ability to communicate with the public and respond to public emergencies.Originality/valueBy analyzing the performance of government microbloggers from the four dimensions of time, content, scale and influence, this paper fills the gap in existing literature on designing the user profiling and role evaluation model of government microbloggers in the context of public emergencies.

Recommendation of Healthcare Services Based on an Embedded User Profile Model

In recent years, as the demand for senior care services has further increased, it has become more difficult to obtain matching services from the vast amount of data. Therefore, this paper proposes a service recommendation framework PCE-CF based on an embedded user portrait model. The framework accurately describes the elderly users through four dimensions—population, society, consumption, and health—and constructs the user portrait model by embedding tags. The embedded vector of each older man is learned through the deep learning model, and different feature groups are meaningfully expressed in the transformation space. In addition, location context and dynamic interest model are introduced to process embedded vectors, and users' service preferences are predicted according to their dynamic behaviors. The experiment results show that the PCE-CF framework proposed in this paper can improve the recommendation algorithm's efficiency and have higher feasibility in personalized service recommendations.

A model for generating a user dynamic profile on social media

A user profile can be considered as an interface of one’s characteristics, interests, thoughts, and preferences. This profile is dynamic as it is changed over time. Designing a user profile model that can track and accommodate these changes is important for providing personalized services on the Internet. This paper introduces a model for building a dynamic user profile based on the daily activities of the user and other users’ activities who are followed by this user. The proposed model tracks the user, learns from his activities, and finally reformulates the user profile. Using two different supervised learning classification algorithms, the user’s activities (tweets, retweets, etc.) are classified into a predefined list of topic categories of interest that are used to form and model the user’s dynamic profile. The primary results show that the proposed model can be used to accurately describe the user, and thus, could be the default user interface for others.

Enhancing malware analysis sandboxes with emulated user behavior

Cybersecurity teams have widely used malware analysis sandboxes to investigate the threat of malware. Correspondingly, armored malware adopts various anti-sandbox techniques to evade analysis, from simple environment-specific traits detection to complex real-user operation environment verification. Particularly, malware may identify sandbox environments by checking several system artifacts that are impacted by the accumulation of normal user activities, such as file accesses. It remains a challenge to defeat this type of anti-sandbox technique. In this paper, we design an emulation-based system called UBER to enhance malware analysis sandboxes. The core idea is to generate realistic system artifacts based on automatically derived user profile models. We solve two major challenges. First, we generate authentic system artifacts continuously to emulate the real-user behaviors. Second, we integrate the generated artifacts stealthily to hide the trace of the emulation. We implement a system prototype using Python system-event monitor and automation control modules. Our experimental results demonstrate that UBER is capable of generating believable system artifacts and effectively mitigates the sandbox evasion techniques that exploit system fingerprinting.

A method of query expansion based on topic models and user profile for search in folksonomy

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

Deep-profiling: a deep neural network model for scholarly Web user profiling

Deep-profiling: a deep neural network model for scholarly Web user profiling

A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design

To proactively fulfill multiple stakeholders’ needs in the engineering solution design process, the knowledge recommendation approach is adopted as a key element in the knowledge management system. Nevertheless, most existing knowledge recommendation approaches cannot simultaneously meet the higher standard of in-context accuracy and diversity. To address the issue, this paper proposes a context-aware diversity-oriented knowledge recommendation approach, thereby assisting stakeholders to accomplish engineering solution design in a smarter manner. Three diversity concerns, namely item-diversity, context-diversity, and user-diversity are addressed by semantic-based content analysis, context definition and awareness, and user profile modeling, respectively. Hence, the proposed approach not only maximizes the diversity of the recommended knowledge but also guarantees its accuracy under multiple problem-solving contexts. Moreover, a practical engineering solution design case on a Smart 3D printer platform is conducted, to validate the efficacy of the proposed approach in providing usable and diverse knowledge items. It is anticipated this work can provide useful insights to practitioners in their knowledge-based engineering solution design process.

Development of an Ontology for the Inclusion of App Users With Visual Impairments

Approximately 15% of the world's population have some form of disability and the majority use apps on their mobile devices to help them in their daily lives with communication, healthcare, or for entertainment purposes. It is not, however, easy for users with impairments to choose the most suitable apps since this will depend on their particular personal characteristics or circumstances in a specific context, and because such users require apps with certain accessibility features which are not always specified in the app description. In order to overcome such difficulties, it is necessary to obtain a user profile that gathers the user's personal details, abilities, disabilities, skills, and interests to facilitate selection. The basis for our research work is to develop an app that recommends a set of apps to users with disabilities. In this respect, the focus of this paper is to obtain a semantic user profile model on which more precise search requests can be performed. The disability we have chosen to concentrate on is that of visual impairment. We propose an ontology-based user profile that matches users' characteristics, disabilities, and interests, and which not only simplifies the classification process but also provides a mechanism for linking them with existing disability ontologies, assistive devices, accessibility concepts, etc. Moreover, thanks to the inclusion of semantic relations and rules, it is possible to reason and infer new information that can be used to make more personalized recommendations than a simple app store search.

STRUCTURAL SIMILARITY MEASURE OF USERS PROFILES BASED ON A WEIGHTED BIPARTITE GRAPHS

Abstract. The user profile is a very important tool in several fields such as recommendation systems, customization systems etc., it is used to narrow the number of data or results provided for a specific user, also to minimize the cost and the time of processing of multiple systems. Whatever the user profile model used, it’s updating and enrichment is a very essential step in the information research process in order to obtain more interesting and satisfactory results, which lead the information systems to develop several techniques aiming to enrich them based especially on similarity methods between user profiles. The similarity methods are used for several tasks such as the detection of duplicate profiles in online social network, also to answer the problem of cold start, and to predict users who can become friends as well as their future intentions, etc. In this paper, we propose a new approach to express the similarity between users profiles by developing a structural similarity measure to calculate the similarity between user profiles based on SimRank measure or similarity ,and the properties of bipartite graphs, in order to take advantage of the information provided by the relational structure between user profiles and their interests, our method is characterized by the similarity propagation between graph's nodes over iterations from source nodes to their successors, so our method finds profiles similar to the query profile, whether the links are direct or indirect between profiles.

Contextual Information Retrieval within Recommender System: Case Study "E-learning System"

This paper focuses on monitoring and analyzing user activities on collaborative filtering -based recommender system in order to guess suitable and unsuitable items' context information using rating matrix which makes more efficient adaptation task. An ontology-based user profile and rules-based context modeling for reasoning about context information is proposed in this research work, in addition to an investigation to apply Semantic Web technologies in user modeling and context reasoning. This proposal is applied in education field in which we have designed an authoring tool for learning objects within ubiquitous environment. This system aims to improve the learning object production task (creation, review, edition…) on behalf of technologies offered by collaborative filtering systems as well as user behaviors monitoring to improve the recommendation process.

A Framework for User Adaptation and Profiling for Social Robotics in Rehabilitation.

Physical rehabilitation therapies for children present a challenge, and its success—the improvement of the patient’s condition—depends on many factors, such as the patient’s attitude and motivation, the correct execution of the exercises prescribed by the specialist or his progressive recovery during the therapy. With the aim to increase the benefits of these therapies, social humanoid robots with a friendly aspect represent a promising tool not only to boost the interaction with the pediatric patient, but also to assist physicians in their work. To achieve both goals, it is essential to monitor in detail the patient’s condition, trying to generate user profile models which enhance the feedback with both the system and the specialist. This paper describes how the project NAOTherapist—a robotic architecture for rehabilitation with social robots—has been upgraded in order to include a monitoring system able to generate user profile models through the interaction with the patient, performing user-adapted therapies. Furthermore, the system has been improved by integrating a machine learning algorithm which recognizes the pose adopted by the patient and by adding a clinical reports generation system based on the QUEST metric.

A Physical Activity Recommender System for Patients With Arterial Hypertension

Recommender systems have been applied in several areas, including e-Health systems, which refers to information and health services enhanced through technology. However, most studies aim at imposing rules to improve lifestyle, rather than recommending nutrition and physical activities. In this context, this study aims to develop a system for recommending physical activities for hypertensive patients to create opportunities for the patients so they can search for and create a healthy lifestyle. To achieve this goal, we elaborated on a hypertensive user profile model, called HyperModel2PAR, and a physical activity recommender system for hypertensive patients, called HyperRecSysPA. The model resulting from this study is composed of 32 elements divided into three groups, which were used in the modeling of user profiles within the system for generating HyperRecSysPA recommendations. The developed system was validated by physicians who answered a specific questionnaire. As a result, ~75% of the recommendations generated were approved. Therefore, this study has prospective contributions to the literature, since both models obtained conclusive results in the assessments performed.