User Modeling Framework Research Articles

HCURec: Hierarchical candidate-aware user modeling for news recommendation

Matching candidate news with user interests is critical for news recommendation. Current studies on news recommendation mainly model a single user interest embedding from the user’s clicked news. One of the major challenges that affects the recommendation is to match candidate news with the user’s multi-field and multi-grained interests. To confront this difficulty, we investigate the multi-grained correlation of user interests and candidate news to obtain their matching features. We propose a hierarchical candidate-aware user modeling framework for news recommendation that matches users’ multi-field and multi-grained interests with candidate news. The framework incorporates candidate news into the modeling of user interests at different levels, namely subcategory-level, category-level and global-level, which learn fine-grained, coarse-grained and overall user interests, respectively. The user interest is finally hierarchically matched at different levels with candidate news to achieve accurate targeting. A collection of experiments were carried out on four real large-scale datasets, and the experimental outcomes demonstrate the supremacy of the proposed method over the existing state-of-the-art methods owing to its highly effective and efficient performance of news recommendation.

Towards privacy-preserving digital marketing: an integrated framework for user modeling using deep learning on a data monetization platform

This paper presents a novel approach to privacy-preserving user modeling for digital marketing campaigns using deep learning techniques on a data monetization platform, which enables users to maintain control over their personal data while allowing marketers to identify suitable target audiences for their campaigns. The system comprises of several stages, starting with the use of representation learning on hyperbolic space to capture the latent user interests across multiple data sources with hierarchical structures. Next, Generative Adversarial Networks are employed to generate synthetic user interests from these embeddings. To ensure the privacy of user data, a Federated Learning technique is implemented for decentralized user modeling training, without sharing data with marketers. Lastly, a targeting strategy based on recommendation system is constructed to leverage the learned user interests for identifying the optimal target audience for digital marketing campaigns. Overall, the proposed approach provides a comprehensive solution for privacy-preserving user modeling for digital marketing.

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

This contribution sheds light on the potential of transparent user models for self-actualization. It discusses the development of EDUSS, a conceptual framework for self-actualization goals of transparent user modeling. Drawing from a qualitative research approach, the framework investigates self-actualization from psychology and computer science disciplines and derives a set of self-actualization goals and mechanisms. Following a human-centered design (HCD) approach, the framework was applied in an iterative process to systematically design a set of interactive visualizations to help users achieve different self-actualization goals in the scientific research domain. For this purpose, an explainable user interest model within a recommender system is utilized to provide various information on how the interest models are generated from users’ publication data. The main contributions are threefold: First, a synthesis of research on self-actualization from different domains. Second, EDUSS, a theoretically-sound self-actualization framework for transparent user modeling consisting of five main goals, namely, Explore, Develop, Understand, Scrutinize, and Socialize. Third, an instantiation of the proposed framework to effectively design interactive visualizations that can support the different self-actualization goals, following an HCD approach.

A Comprehensive User Modeling Framework and a Recommender System for Personalizing Well-Being Related Behavior Change Interventions: Development and Evaluation

Health recommender systems (HRSs) have the potential to effectively personalize well-being related behavior change interventions to the needs of individuals. However, personalization is often considered from a narrow perspective, and the underlying user features are inconsistent across HRSs. Particularly, theory-based determinants of behavior and the variety of lifestyle domains influencing well-being are poorly addressed. We propose a comprehensive, theory-based framework of user features, a virtual individual (VI) model, to support the extensive personalization of digital well-being interventions. We introduce a prototype HRS (With-Me HRS), which uses knowledge-based filtering and recommends behavior change objectives and activities from several lifestyle domains. With-Me HRS realizes a minimum set of important VI model features related to well-being, lifestyle, and behavioral intention. We report the preliminary validity and usefulness of the HRS, evaluated in a real-life health-coaching program with 50 participants. The recommendations were used in decision-making for half of the participants and hidden for others. For 73% of the participants (85% with visible vs. 62% with hidden recommendations), at least one of the recommended activities was included into their coaching plans. The HRS reduced coaches’ perceived effort in identifying appropriate coaching tasks for the participants (effect size: Vargha-Delaney Â=0.71, 95% CI 0.59-0.84) but not in identifying behavior change objectives. From the participants’ perspective, the quality of coaching improved (effect size for one of three quality metrics: Â=0.71, 95% CI 0.57-0.83). These results provide a baseline for testing the influence of additional user model features on the validity of recommendations generated by knowledge-based multi-domain HRSs.

Modeling user interest in social media using news media and wikipedia

Social media has become an important source of information and a medium for following and spreading trends, news, and ideas all over the world. Although determining the subjects of individual posts is important to extract users' interests from social media, this task is nontrivial because posts are highly contextualized and informal and have limited length. To address this problem, we propose a user modeling framework that maps the content of texts in social media to relevant categories in news media. In our framework, the semantic gaps between social media and news media are reduced by using Wikipedia as an external knowledge base. We map term-based features from a short text and a news category into Wikipedia-based features such as Wikipedia categories and article entities. A user's microposts are thus represented in a rich feature space of words. Experimental results show that our proposed method using Wikipedia-based features outperforms other existing methods of identifying users' interests from social media.

Evaluating the Performance Estimators via Machine Learning Supervised Learning Algorithms for Dataset Threshold

Framework for user modeling is represented that is useful for both supervised and unsupervised machine learning techniques which will reduce the cost of development that is typically related to the knowledge-based approaches of machine learning for supervised approaches and user modeling that is basically required for the handling of the label-data. Experimental data is used for Research in bioinformatics. Vast amounts of experimental data populate the Current biological databases. Bioinformatics uses the machine learning concepts and has attained a lot of success in this research field. We focus on semisurprised framework which incorporates labeled and unlabeled data in the general-purpose learner. Some of transfer graph, learning algorithms and the standard methods that include support vector machines and as a special case the regularized least squares can be obtained. We can use properties of reproducing the kernel Hilbert space to prove the new. Represented theorems provide the theoretical base for algorithms.

A framework for user modeling in QuizMASter

This paper presents an approach to user modeling in QuizMASter, a multi-user educational game shows that uses multi-agent systems to create a personalized learning environment. To keep the students motivated during the games, the system creates, maintains, and uses models of all attending contestants (i.e., learners). Taking advantage of prominent student modeling techniques with some novel ideas, we propose a structure and procedure to create student models and combined student models in QuizMASter. These models are used to create an adaptive environment in the quiz games, selecting and posing questions whose levels of difficulty closely match the knowledge levels of game contestants in certain areas of knowledge; meaning that the game questions for each game are adjusted to the knowledge levels of the learners and the game does not provide too easy or too difficult questions for the contestants. By selecting properly challenging questions for each game show, students are expected to stay motivated and continue benefiting from using the game show, increasing their knowledge through game-based learning.

Virtual user models for the elderly and disabled for automatic simulated accessibility and ergonomy evaluation of designs

This paper presents a framework for automatic simulated accessibility and ergonomy testing of virtual prototypes of products using virtual user models. The proposed virtual user modeling framework describes virtual humans focusing on the elderly and people with disabilities. Geometric, kinematic, physical, behavioral and cognitive aspects of the user affected by possible disabilities are examined, in order to create virtual user models able to represent people with various functional limitations. Hierarchical task and interaction models are introduced, in order to describe the user's capabilities at multiple levels of abstraction. The use of alternative ways of a user task's execution, exploiting different modalities and assistive devices, is supported by the proposed task analysis. Experimental results on the accessibility and ergonomy evaluation of different workplace designs for the use of a telephone and a stapler show how the proposed framework can be put into practice and demonstrate its significant potential.

A PLA-based privacy-enhancing user modeling framework and its evaluation

Reconciling personalization with privacy has been a continuing interest in user modeling research. This aim has computational, legal and behavioral/attitudinal ramifications. We present a dynamic privacy-enhancing user modeling framework that supports compliance with users' individual privacy preferences and with the privacy laws and regulations that apply to each user. The framework is based on a software product line architecture. It dynamically selects personalization methods during runtime that meet the current privacy constraints. Since dynamic architectural reconfiguration is typically resource-intensive, we conducted a performance evaluation with four implementations of our system that vary two factors. The results demonstrate that at least one implementation of our approach is technically feasible with comparatively modest additional resources, even for websites with the highest traffic today. To gauge user reactions to privacy controls that our framework enables, we also conducted a controlled experiment that allowed one group of users to specify privacy preferences and view the resulting effects on employed personalization methods. We found that users in this treatment group utilized this feature, deemed it useful, and had fewer privacy concerns as measured by higher disclosure of their personal data.

An Ontology-Based Framework for Modeling User Behavior—A Case Study in Knowledge Management

This paper focuses on the role of user modeling and semantically enhanced representations for personalization. This paper presents a generic Ontology-based User Modeling framework (OntobUMf), its components, and its associated user modeling processes. This framework models the behavior of the users and classifies its users according to their behavior. The user ontology is the backbone of OntobUMf and has been designed according to the Information Management System Learning Information Package (IMS LIP). The user ontology includes a Behavior concept that extends IMS LIP specification and defines characteristics of the users interacting with the system. Concrete examples of how OntobUMf is used in the context of a Knowledge Management (KM) System are provided. This paper discusses some of the implications of ontology-based user modeling for semantically enhanced KM and, in particular, for personal KM. The results of this research may contribute to the development of other frameworks for modeling user behavior, other semantically enhanced user modeling frameworks, or other semantically enhanced information systems.

Visualization and User-Modeling for Browsing Personal Photo Libraries

We present a user-centric system for visualization and layout for content-based image retrieval. Image features (visual and/or semantic) are used to display retrievals as thumbnails in a 2-D spatial layout or “configuration” which conveys all pair-wise mutual similarities. A graphical optimization technique is used to provide maximally uncluttered and informative layouts. Moreover, a novel subspace feature weighting technique can be used to modify 2-D layouts in a variety of context-dependent ways. An efficient computational technique for subspace weighting and re-estimation leads to a simple user-modeling framework whereby the system can learn to display query results based on layout examples (or relevance feedback) provided by the user. The resulting retrieval, browsing and visualization can adapt to the user's (time-varying) notions of content, context and preferences in style and interactive navigation. Monte Carlo simulations with machine-generated layouts as well as pilot user studies have demonstrated the ability of this framework to model or “mimic” users, by automatically generating layouts according to their preferences.

A shell for developing non-monotonic user modeling systems

This paper first presents a general structured framework for user modeling, which includes a set of basic user modeling purposes exploited by a user modeling system when providing a set of services to other components of an application. At a higher level of abstraction such an application may perform a generic user modeling task , which results from an appropriate combination of some basic user modeling purposes. The central aim of the paper is to present, within the proposed framework, a flexible general-purpose shell, called UMT (User Modeling Tool), which supports the development of user modeling applications. UMT features a non-monotonic approach for performing the modeling activity: more specifically, it utilizes a modeling approach called assumption-based user modeling, which exploits a truth maintenance mechanism for maintaining the consistency of the user model. The modeling task is divided into two separate activities, one devoted to user classification and user model management, and the other devoted to consistency maintenance of the model. The modeling knowledge exploited by UMT is represented by means of stereotypes and production rules. UMT is capable of identifying, at any given moment during an interaction, all the possible alternative models which adequately describe the user and are internally consistent. The choice of the most plausible one among them is then performed using an explicit programmable preference criterion. UMT is also characterized by a very well defined and simple interface with the hosting application, and by a specialized development interface which supports the developer during the construction of specific applications. This paper includes an example application in the field of information-providing systems. UMT has been developed in Common LISP.