User Modeling Research Papers - Academia.edu (original) (raw)

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- Information Systems, User Modeling, Machine Learning, Methodology

This paper describes the use of adaptation patterns in the task of formulating standards for adaptive educational hypermedia (AEH) systems that is currently under investigation by the EU ADAPT project. Within this project, design dimensions for high granularity patterns have been established. In this paper we focus on detailing lower granularity adaptive patterns based upon learning styles. Several patterns from existing AEH system case studies are identified and classified according to an extended learning style "onion" model. This model forms the basis of a learning style taxonomy, introduced here, whose components determine adaptation patterns for AEH. These patterns are of importance both for authoring, as well as for interfacing between adaptive hypermedia systems. From an authoring point of view, these patterns may be used to establish a fine-grain approach to instructional strategies that can be implemented in AEH systems, as a response to a particular learning style. The implementation of this adaptation pattern taxonomy is discussed, both generally and in detail.

A common practice in modeling affect from physiological signals consists of reducing the signals to a set of statistical features that feed predictors of self-reported emotions. This paper analyses the impact of various timewindows, used for the extraction of physiological features, to the accuracy of affective models of players in a simple 3D game. Results show that the signals recorded in the central part of a short gaming experience contain more relevant information to the prediction of positive affective states than the starting and ending parts while the relevant information to predict anxiety and frustration appear not to be localized in a specific time interval but rather dependent on particular game stimuli.

- by Héctor P. Martínez
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- Psychophysiology, User Modeling, Affective Computing, Player Experience

Nowadays modern society requires that every citizen always updates and improves her/his knowledge and skills necessary to working and researching. E-learning or distance learning gives everyone a chance to study at anytime and anywhere with full support of computer technology and network. Adaptive learning, a variant of e-learning, aims to satisfy the demand of personalization in learning. Learners’ information and characteristics such as knowledge, goal, experience, interest, and background are the most important to adaptive system. These characteristics are organized in a structure called learner model (or user model) and the system or computer software that builds up and manipulates learner model is called user modeling system or learner modeling system. In this book, I propose a learner model that consists of three essential kinds of information about learners such as knowledge, learning style and learning history. Such three characteristics form a triangle and so this learner model is called Triangular Learner Model (TLM). The book contains seven chapters, which covers mathematical features of TLM. Chapter I is a survey of user model, user modeling, and adaptive learning. Chapter II introduces the general architecture of the proposed TLM and a user modeling system named Zebra. Chapter III, IV, V describes three sub-models of TLM such as knowledge sub-model, learning style sub-model, and learning history sub-model in full of mathematical formulas and fundamental methods. Chapter VI gives some approaches to evaluate TLM and Zebra. Chapter VII summarizes the research and discusses future trend of Zebra.

- by Loc Nguyen's Academic Network
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- User Modeling, Adaptive learning
- by Sirinya ON-AT
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- User Modeling, Snowball Sampling, Social Network Analysis

Naive Bayes is a relatively simple classification method to, e.g., rate TV programs as interesting or uninteresting to a user. In case the training set consists of instances, chosen randomly from the instance space, the posterior probability estimates are random variables. Their statistical properties can be used to calculate confidence intervals around them, enabling more refined classification strategies than the usual argmax-operator. This may alleviate the cold-start problem and provide additional feedback to the user. In this paper, we give an explicit expression to estimate the variances of the posterior probability estimates from the training data and investigate the strategy that refrains from classification in case the confidence interval around the largest posterior probability overlaps with any of the other intervals. We show that the classification error rate can be significantly reduced at the cost of a lower coverage, i.e., the fraction of classifiable instances, in a TV-program recommender.

- by W. Verhaegh
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- User Modeling, Machine Learning, Reliability, Confidence intervals
- by Silvio Cazella
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- User Modeling, Multiagent Systems, Tese

Learning style models constitute a valuable tool for improving individual learning by the use of adaptation techniques based on them. In this paper, we present how the benefit of considering learning styles with adaptation purposes, as part of the user model, can be extended to the context of collaborative learning as a key feature for group formation. We explore the effects that the combination of students with different learning styles in specific groups may have in the final results of the tasks accomplished by them collaboratively. With this aim, a case study with 166 students of computer science has been carried out, from which conclusions are drawn. We also describe how an existing web-based system can take advantage of learning style information in order to form more productive groups. Our ongoing work concerning the automatic extraction of grouping rules starting from data about previous interactions within the system is also outlined. Finally, we present our challenges, related to the continuous improvement of collaboration by the use and dynamic modification of automatic grouping rules.

- by Estefanía Martín
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- Information Systems, User Modeling, Adaptation, Continuous Improvement

Dynamic aspects of Information Retrieval (IR), including changes found in data, users and systems, are increasingly being utilized in search engines and information filtering systems. Existing IR techniques are limited in their ability to optimize over changes, learn with minimal computational footprint and be responsive and adaptive. The objective of this tutorial is to provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling, the statistical modeling of IR systems that can adapt to change. It will cover techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and a handful of useful algorithms and tools for solving IR problems incorporating dynamics.

The function of recommender systems, after all, is to help people make better choices. So you might expect work in this area to be based on a clear understanding of how people make choices and how these processes can be supported by recommender systems. But in fact we see only occasional attention to the psychology of choice and decision making in this area. One reason is that the most relevant knowledge is scattered around a number of areas of psychological research, including judgment and decision making, behavioral economics, social influence, habitual behavior, and learning. This talk will give a sample of key concepts and results from these areas, showing how they suggest new research issues and design ideas for those who work on recommender systems.

- by Irene Mazzotta
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- User Modeling

Creating an efficient user knowledge model is a crucial task for web-based adaptive learning environments in different domains. It is often a challenge to determine exactly what type of domain dependent data will be stored and how it will be evaluated by a user modeling system. The most important disadvantage of these models is that they classify the knowledge of users without taking into account the weight differences among the domain dependent data of users. For this purpose, both the probabilistic and the instance-based models have been developed and commonly used in the user modeling systems. In this study a powerful, efficient and simple ‘Intuitive Knowledge Classifier’ method is proposed and presented to model the domain dependent data of users. A domain independent object model, the user modeling approach and the weight-tuning method are combined with instance-based classification algorithm to improve classification performances of well-known the Bayes and the k-nearest neighbor-based methods. The proposed knowledge classifier intuitively explores the optimum weight values of students’ features on their knowledge class first. Then it measures the distances among the students depending on their data and the values of weights. Finally, it uses the dissimilarities in the classification process to determine their knowledge class. The experimental studies have shown that the weighting of domain dependent data of students and combination of user modeling algorithms and population-based searching approach play an essential role in classifying performance of user modeling system. The proposed system improves the classification accuracy of instance-based user modeling approach for all distance metrics and different k-values.

- by Ricardo Baeza-Yates
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- Food Science, User Modeling, Implicit learning, Sentiment Analysis

Research on recommender systems typically focuses on the accuracy of prediction algorithms. Because accuracy only partially constitutes the user experience of a recommender system, this paper proposes a framework that takes a user-centric approach to recommender system evaluation. The framework links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively). Furthermore, it incorporates the influence of personal and situational characteristics on the user experience. This paper reviews how current literature maps to the framework and identifies several gaps in existing work. Consequently, the framework is validated Equation Modeling. The results of these studies show that subjective system aspects and experience variables are invaluable in explaining why and how the user experience of recommender systems comes about. In all studies we observe that perceptions of recommendation quality and/or variety are important mediators in predicting the effects of objective system aspects on the three components of user experience: process (e.g. perceived effort, difficulty), system (e.g. perceived system effectiveness) and outcome (e.g. choice satisfaction). Furthermore, we find that these subjective aspects have strong and sometimes interesting behavioral correlates (e.g. reduced browsing indicates higher system effectiveness). They also show several tradeoffs between system aspects and personal and situational characteristics (e.g. the amount of preference feedback users provide is a tradeoff between perceived system usefulness and privacy concerns). These results, as well as the validated framework itself, provide a platform for future research on the user-centric evaluation of recommender systems.

- by Bart Knijnenburg
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- Information Systems, User Modeling, User eXperience, Recommender System

Recent studies have shown that the use of educational games during learning process is dramatically increased. Furthermore, researchers suggest the attachment of adaptive features in order to motivate students and assess their knowledge level on a specific educational subject. In this paper, we present an educational browser-based game with coins that contributes to understanding better the addition process in elementary education. The game encompasses user modeling and adaptive techniques. It determines students’ knowledge level and helps them outcome difficulties and obtain fluency in arithmetic addition skills.

- by Spyros Papadimitriou
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- User Modeling, Arithmetic, Adaptive Educational Games

Our research agenda focuses on building software agents that can employ user modeling techniques to facilitate information access and management tasks. Personal assistant agents embody a clearly beneficial application of intelligent agent technology. A particular kind of assistant agents, recommender systems, can be used to recommend items of interest to users. To be successful, such systems should be able to

- by Sandip Sen
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- Information Systems, User Modeling, Research Agenda, Intelligent Agent
- by Stephan Weibelzahl
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- User Modeling, Experimental Design, Adaptive System, Empirical Evaluation

Literature on the modeling and simulation of complex adaptive systems (cas) has primarily advanced vertically in different scientific domains with scientists developing a variety of domain-specific approaches and applications. However, while cas researchers are inherently interested in an interdisciplinary comparison of models, to the best of our knowledge, there is currently no single unified framework for facilitating the development, comparison, communication and validation of models across different scientific domains. In this thesis, we propose first steps towards such a unified framework using a combination of agent-based and complex network-based modeling approaches and guidelines formulated in the form of a set of four levels of usage, which allow multidisciplinary researchers to adopt a suitable framework level on the basis of available data types, their research study objectives and expected outcomes, thus allowing them to better plan and conduct their respective research case studies. Firstly, the complex network modeling level of the proposed framework entails the development of appropriate complex network models for the case where interaction data of cas components is available, with the aim of detecting emergent patterns in the cas under study. The exploratory agent-based modeling level of the proposed framework allows for the development of proof-of-concept models for the cas system, primarily for purposes of exploring feasibility of further research. Descriptive agent-based modeling level of the proposed framework allows for the use of a formal step-by-step approach for developing agent-based models coupled with a quantitative complex network and pseudocode-based specification of the model, which will, in turn, facilitate interdisciplinary cas model comparison and knowledge transfer. Finally, the validated agent-based modeling level of the proposed framework is concerned with the building of in-simulation verification and validation of agent-based models using a proposed Virtual Overlay Multiagent System approach for use in a systematic team-oriented approach to developing models. The proposed framework is evaluated and validated using seven detailed case study examples selected from various scientific domains including ecology, social sciences and a range of complex adaptive communication networks. The successful case studies demonstrate the potential of the framework in appealing to multidisciplinary researchers as a methodological approach to the modeling and simulation of cas by facilitating effective communication and knowledge transfer across scientific disciplines without the requirement of extensive learning curves.

Every student has individual features such as knowledge, goals, experiences, interests, backgrounds, personal traits, learning styles, learning activities, and study results. User model or learner model is constructed from these features. The process to build up learner model is called user modeling process or learner modeling process. Adaptive learning system uses learner model to make adaptation. In other words, adaptive learning system takes advantages individual information available in learner model in order to tailor learning materials (lessons, exercises, tests, etc.) and teaching methods to each student. Anyway, learner model is very important to adaptive learning system and other adaptive applications. This study report focuses on learner model, which is extracted from the master thesis of “User Modeling and User Proﬁling in Adaptive E-learning Systems” of author Christoph Fröschl. I express my deep gratitude to the author Christoph Fröschl for providing her/his great research.

- by Loc Nguyen's Academic Network and +1
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- User Modeling, Learner Model, Learner Modeling, Adaptive learning

Feedback on player experience and behaviour can be invaluable to game designers, but there is need for specialised knowledge discovery tools to deal with high volume playtest data. We describe a study with a commercial third-person shooter, in which integrated player activity and experience data was captured and mined for design-relevant knowledge. We demonstrate that association rule learning and rule templates can be used to extract meaningful rules relating player activity and experience during combat. We ...

- by Paul Cairns
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- User Modeling, User Experience (UX), Player Experience, Game Analytics

Considering the increasingly complex media landscape and diversity of use, it is important to establish a common ground for identifying and describing the variety of ways in which people use new media technologies. Characterising the nature of media-user behaviour and distinctive user types is challenging and the literature offers little guidance in this regard. Hence, the present research aims to classify diverse user behaviours into meaningful categories of user types, according to the frequency of use, variety of use and content preferences. To reach a common framework, a review of the relevant research was conducted. An overview and meta-analysis of the literature (22 studies) regarding user typology was established and analysed with reference to (1) method, (2) theory, (3) media platform, (4) context and year, and (5) user types. Based on this examination, a unified Media-User Typology (MUT) is suggested. This initial MUT goes beyond the current research literature, by unifying all the existing and various user type models. A common MUT model can help the Human-Computer Interaction community to better understand both the typical users and the diversification of media-usage patterns more qualitatively. Developers of media systems can match the users' preferences more precisely based on an MUT, in addition to identifying the target groups in the developing process. Finally, an MUT will allow a more nuanced approach when investigating the association between media usage and social implications such as the digital divide.

- by Audrey Perem
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- Information Systems, Psychology, Cognitive Science, Human Computer Interaction

ABSTRACT The importance and concern given to the autonomy and independence of elderly people and patients suffering from some kind of disability has been growing significantly in the last few decades. Intelligent wheelchairs (IW) are technologies that can increase the autonomy and independence of this kind of population and are nowadays a very active research area. This paper presents a Data Analysis System (DAS) that provides an adapted command language to an user of the IW. This command language is a set of input sequences that can be created using inputs from an input device or a combination of the inputs available in a multimodal interface. The results show that there are statistical evidences to affirm that the mean of the evaluation of the DAS is higher than the mean of the evaluation of the command language recommend by the health specialist (p value = 0.002) with a sample of 11 cerebral palsy users. This work demonstrates that it is possible to adapt an intelligent wheelchair interface to the user even when the users present heterogeneous and severe physical constraints.

- by Mónica Faria
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- Computer Science, Artificial Intelligence, Geriatrics, User Modeling

- by Loc Nguyen's Academic Network and +1
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- Computer Science, Software Engineering, Education, User Modeling

In this paper we are going to show how to discover interesting prediction rules from student usage information to improve adaptive web courses. We have used AHA! to make courses that adapt both the presentation and the navigation depending on the level of knowledge that each particular student has. We have performed several modifications in AHA! to specialize it and power it in the educational area. Our objective is to discover relations between all the picked-up usage data (reading times, difficulty levels and test results) from student executions and show the most interesting to the teacher so that he can carry out the appropriate modifications in the course to improve it.

- by carlos castro
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- User Modeling, UM

Individual differences in cognitive flexibility may underlie a variety of different user behaviors, but a lack of effective measurement tools has limited the predictive and descriptive potential of cognitive flexibility in humancomputer interaction applications. This study presents a new computerized measure of cognitive flexibility, and then provides evidence for convergent validity. Our findings indicate moderate to strong correlations with the Trail Making Task, and in particular, those aspects of the task most closely associated with cognitive flexibility. Results of this study provide support for the validity of a new measure of cognitive flexibility. We conclude by discussing the measure's potential applicability in the field of HCI.

- by Ivonne Figueroa
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- User Modeling, Individual Differences, Cognitive Flexibility

Internet advertising is a fast growing business which has proved to be significantly important in digital economics. It is vitally important for both web search engines and online content providers and publishers because web advertising provides them with major sources of revenue. Its presence is increasingly important for the whole media industry due to the influence of the Web. For advertisers, it is a smarter alternative to traditional marketing media such as TVs and newspapers. As the web evolves and data collection continues, the design of methods for more targeted, interactive, and friendly advertising may have a major impact on the way our digital economy evolves, and to aid societal development.
Towards this goal mathematically well-grounded Computational Advertising methods are becoming necessary and will continue to develop as a fundamental tool towards the Web. As a vibrant new discipline, Internet advertising requires effort from different research domains including Information Retrieval, Machine Learning, Data Mining and Analytic, Statistics, Economics, and even Psychology to predict and understand user behaviours. In this paper, we provide a comprehensive survey on Internet advertising, discussing and classifying the research issues, identifying the recent technologies, and suggesting its future directions. To have a comprehensive picture, we first start with a brief history, introduction, and classification of the industry and present a schematic view of the new advertising ecosystem. We then introduce four major participants, namely advertisers, online publishers, ad exchanges and web users; and through analysing and discussing the major research problems and existing solutions from their perspectives respectively, we discover and aggregate the fundamental problems that characterise the newly-formed research field and capture its potential future prospects.

- by Marc Sloan
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- Information Retrieval, User Modeling, Computational Advertising

This paper presents a new method for sentiment analysis in Facebook that, starting from messages written by users, supports: (i) to extract information about the users' sentiment polarity (positive, neutral or negative), as transmitted in the messages they write; and (ii) to model the users' usual sentiment polarity and to detect significant emotional changes. We have implemented this method in SentBuk, a Facebook application also presented in this paper. SentBuk retrieves messages written by users in Facebook and classifies them according to their polarity, showing the results to the users through an interactive interface. It also supports emotional change detection, friend's emotion finding, user classification according to their messages, and statistics, among others. The classification method implemented in SentBuk follows a hybrid approach: it combines lexical-based and machine-learning techniques. The results obtained through this approach show that it is feasible to perform sentiment analysis in Facebook with high accuracy (83.27%). In the context of e-learning, it is very useful to have information about the users' sentiments available. On one hand, this information can be used by adaptive e-learning systems to support personalized learning, by considering the user's emotional state when recommending him/her the most suitable activities to be tackled at each time. On the other hand, the students' sentiments towards a course can serve as feedback for teachers, especially in the case of online learning, where face-to-face contact is less frequent. The usefulness of this work in the context of e-learning, both for teachers and for adaptive systems, is described too.

- by Alvaro Ortigosa
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- Information Systems, Psychology, Cognitive Science, Social Networks

Telecommunications fraud not only burdens telecom provider's accountings but burdens individual users as well. The latter are particularly affected in the case of superimposed fraud where the fraudster uses a legitimate user's account in parallel with the user. These cases are usually identified after user complaints for excess billing. However, inside the network of a large firms or organization, superimposed fraud may go undetected for some time. The present paper deals with the detection of fraudulent telecom activity inside large organizations' premises. Focus is given on superimposed fraud detection. The problem is attacked via the construction of an expert system which incorporates both the network administrator's expert knowledge and knowledge derived from the application of data mining techniques on real world data.

- by Constantinos Hilas
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- Expert Systems, User Modeling, Data Mining, Telecommunications
- by Georgios Christou
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- User Modeling, Usability, Interaction Design, HCI

Adaptive learning systems require well-organized user model along with solid inference mechanism. Overlay modeling is the method in which the domain is decomposed into a set of elements and the user model is simply a set of masteries over those elements. The combination between overlay model and Bayesian network (BN) will make use of the flexibility and simplification of overlay modeling and the power inference of BN. Thus it is compulsory to pre-define parameters, namely, Conditional Probability Tables (CPT (s)) in BN but no one ensured absolutely the correctness of these CPT (s). This research focuses on how to enhance parameters’ quality in Bayesian overlay model, in other words, this is the evolution of CPT(s).

- by Loc Nguyen's Academic Network and +1
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- User Modeling, Learner Model, Adaptive learning, Bayesian Network

Abstract Preference elicitation is a challenging fundamental problem when designing recommender systems. In the present work we propose a content-based technique to automatically generate a semantic representation of the user's musical preferences directly from audio. Starting from an explicit set of music tracks provided by the user as evidence of his/her preferences, we infer high-level semantic descriptors for each track obtaining a user model. To prove the benefits of our proposal, we present two applications of our technique ...

- by Dmitry Bogdanov
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- Information Systems, User Modeling, Evaluation, Information Processing

The results presented in this paper come from an exploratory study of 19,379 mind maps created by 11,179 users from the mind mapping applications ‘Docear’ and ‘MindMeister’. The objective was to find out how mind maps are structured and which information they contain. Results include: A typical mind map is rather small, with 31 nodes on average (median), whereas each node usually contains between one to three words. In 66.12% of cases there are few notes, if any, and the number of hyperlinks tends to be rather low, too, but depends upon the mind mapping application. Most mind maps are edited only on one (60.76%) or two days (18.41%). A typical user creates around 2.7 mind maps (mean) a year. However, there are exceptions which create a long tail. One user created 243 mind maps, the largest mind map contained 52,182 nodes, one node contained 7,497 words and one mind map was edited on 142 days.

Rescue robotics has been suggested by a recent DARPA/NSF study as an application domain for the research in human-robot integration (HRI). This article provides a short tutorial on how robots are currently used in urban search and rescue (USAR) and discusses the HRI issues encountered over the past eight years. A domain theory of the search activity is formulated. The domain theory consists of two parts: (1) a workflow model identifying the major tasks, actions, and roles in robot-assisted search (e.g., a workflow model) and (2) a general information flow model of how data from the robot is fused by various team members into information and knowledge. The information flow model also captures the types of situation awareness needed by each agent in the rescue robot system. The article presents a synopsis of the major HRI issues in reducing the number of humans it takes to control a robot, maintaining performance with geographically distributed teams with intermittent communications, and encouraging acceptance within the existing social structure.

- by Githin John
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- Engineering, Robotics, Artificial Intelligence, Human Computer Interaction

This paper describes the development of an educational adventure game which aims to be useful in learning arithmetic. The main game platform communicates with several arithmetic mini games. A student modeling component is used to create a profile for every student and grant adaptive features to the main game platform. Additionally, an error diagnosis component is also useful for evaluating students' errors and giving relative advice. The choice of adventure game genre was based to past research which revealed that adventure games can have beneficial effects in learning process.

- by Spyros Papadimitriou
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- User Modeling, Edutainment, Educational Games, Arithmetic

Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built. The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method is capable of picking subsets of features that generate more accurate affective models.

Considering the increasingly complex media landscape and diversity of use, it is important to establish a common ground for identifying and describing the variety of ways in which people use new media technologies. Characterising the nature of media-user behaviour and distinctive user types is challenging and the literature offers little guidance in this regard. Hence, the present research aims to classify diverse user behaviours into meaningful categories of user types, according to the frequency of use, variety of use and content preferences. To reach a common framework, a review of the relevant research was conducted. An overview and meta-analysis of the literature (22 studies) regarding user typology was established and analysed with reference to (1) method, (2) theory, (3) media platform, (4) context and year, and (5) user types. Based on this examination, a unified Media-User Typology (MUT) is suggested. This initial MUT goes beyond the current research literature, by unifying all the existing and various user type models. A common MUT model can help the Human–Computer Interaction community to better understand both the typical users and the diversification of media-usage patterns more qualitatively. Developers of media systems can match the users’ preferences more precisely based on an MUT, in addition to identifying the target groups in the developing process. Finally, an MUT will allow a more nuanced approach when investigating the association between media usage and social implications such as the digital divide.

- by Petter Bae Brandtzaeg
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- Media Studies, New Media, Behavioral Sciences, User Modeling

The concept of personal learning environments has become a significant research topic over the past few years. Building such personal, adaptive environments requires the convergence of several modeling dimensions and an interaction strategy based on a user model that incorporates key cognitive characteristics of the learners. This paper reports on an initial study carried out to evaluate the extent to which matching the interface design to the learner cognitive style facilitates learning performance. Results show that individual differences influence the way learners react to and perform under different interface conditions, however no simple effects were observed that confirm a relationship between cognitive style and interface affect.

- by Lachlan Mackinnon
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- User Modeling

Canada There has been a continuous increase in the design and application of computer games for purposes other than entertainment in recent years. Serious games—games that motivate behaviour and retain attention in serious contexts — can change the attitudes, behaviours, and habits of players. These games for change have been shown to motivate behaviour change, persuade people, and promote learning using various persuasive strategies. However, persuasive strategies that motivate one player may demotivate another. In this article, we show the importance of tailoring games for change in the context of a game designed to improve healthy eating habits. We tailored a custom-designed game by adapting only the persuasive strategies employed; the game mechanics themselves did not vary. Tailoring the game design to players' personality type improved the effectiveness of the games in promoting positive attitudes, intention to change behaviour, and self-efficacy. Furthermore, we show that the benefits of tailoring the game intervention are not explained by the improved player experience, but directly by the choice of persuasive strategy employed. Designers and researchers of games for change can use our results to improve the efficacy of their game-based interventions.

- by Rita Orji
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- User Modeling, Serious Games, Video Games, Personalization

In the past few years, the growing number of personal information shared on the Web (through Web 2.0 applications) increased awareness regarding privacy and personal data. Recent studies showed that privacy in Social Networks is a major concern when user profiles are publicly shared, revealing that most users are aware of privacy settings. Most Social Networks provide privacy settings restricting access to private data to those who are in the user’s friends lists (i.e. their “social graph”) such as Facebook’s privacy preferences. Yet, the studies show that users require more complex privacy settings as current systems do not meet their requirements. Hence, we propose a platform-independent system that allows end-users to set fine-grained privacy preferences for the creation of privacy-aware faceted user profiles on the Social Web.

- by F. Orlandi
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- Semantic Web Technologies, User Modeling, Semantics, Privacy

This thesis introduced two novel reputation models to generate accurate item reputation scores using ratings data and the statistics of the dataset. It also presented an innovative method that incorporates reputation awareness in recommender systems by employing voting system methods to produce more accurate top-N item recommendations. Additionally, this thesis introduced a personalisation method for generating reputation scores based on users' interests, where a single item can have different reputation scores for different users. The personalised reputation scores are then used in the proposed reputation-aware recommender systems to enhance the recommendation quality.

- by Ahmad Abdel-Hafez
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- Electrical Engineering, Mathematics, Computer Science, Physics

The development of an artificial agent-driven rescue robot capable of interacting naturally with humans under urban emergency conditions brings out a number of issues that need to be addressed: among these are (i) providing a full description and understanding of the user's perspective, and thus creating a reliable and functional user model, (ii) addressing the question of appearance and design, including the interface to be implemented, and (iii) evaluating the reliability and credibility of such a robot under conditions of panic, both in terms of group and personal panic reactions to stressful, extreme conditions. This article addresses these topics with a set of suggestions for future research in the field. In particular, we propose a research agenda focused on the appearance of the humanoid agent, and its trustworthiness under circumstances of panic in emergency situations, together with suggestions for experiments aimed at informing a user model of crowd and individual behaviour in emergency situations.

- by Nicla Rossini
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- Robotics, Emotion, Interpersonal Communication, Social Networks

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on "mobile data science and intelligent apps" in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-today situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

- by Dr. Iqbal H. Sarker
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- Artificial Intelligence, User Modeling, Machine Learning, Mobile Technology

This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used to build a data-driven predictive model. The traditional decision tree model typically creates a number of leaf nodes as decision nodes that represent context-specific rigid decisions, and consequently may cause overfitting problem in behavior modeling. However, in many practical scenarios within the context-aware environment, the generalized outcomes could play an important role to effectively capture user behavior. In this paper, we propose a behavioral decision tree, "BehavDT" context-aware model that takes into account user behavior-oriented generalization according to individual preference level. The BehavDT model outputs not only the generalized decisions but also the context-specific decisions in relevant exceptional cases. The effectiveness of our BehavDT model is studied by conducting experiments on individual user real smartphone datasets. Our experimental results show that the proposed BehavDT context-aware model is more effective when compared with the traditional machine learning approaches, in predicting user diverse behaviors considering multi-dimensional contexts.

- by Dr. Iqbal H. Sarker and +1
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- Artificial Intelligence, User Modeling, Machine Learning, Data Mining

We present a user modeling system that serves as the foundation of a personal assistant. The system ingests web search history for signed-in users, and identifies coherent contexts that correspond to tasks, interests, and habits. Unlike past work which focused on either in-session tasks or tasks over a few days, we look at several months of history in order to identify not just short-term tasks, but also long-term interests and habits. The features we use for identifying coherent contexts yield substantially higher precision and recall than past work. We also present an algorithm for identifying contexts that is 8 to 30 times faster than previous algorithms. The user modeling system has been deployed in production. It runs over hundreds of millions of users, and updates the models with a 10-minute latency. The contexts identified by the system serve as the foundation for generating recommendations in Google Now.

- by Vineet Gupta
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- User Modeling

This paper examines location-based services (LBS) from a broad perspective involving deWnitions, characteristics, and application prospects. We present an overview of LBS modeling regarding users, locations, contexts and data. The LBS modeling endeavors are cross-examined with a research agenda of geographic information science. Some core research themes are brieXy speculated.

- by Georg Gartner
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- Services, User Modeling, Modeling, Localization

Many studies have investigated personalized information presentation in the context of mobile museum guides. In order to provide such a service, information about museum visitors has to be collected and visitors have to be monitored and modelled in a non-intrusive manner. This can be done by using known museum visiting styles to classify the visiting style of visitors as they

- by Dina Goren-bar and +2
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- User Modeling, Unsupervised Learning, Qualitative Analysis, Empirical evidence

It is a widely held assumption that learning style is a useful model for quantifying user characteristics for effective personalized learning. We set out to challenge this assumption by discussing the current state of the art, in relation to quantitative evaluations of such systems and also the methodologies that should be employed in such evaluations. We present two case studies that provide rigorous and quantitative evaluations of learning-style-adapted e-learning environments. We believe that the null results of both these studies indicate a limited usefulness in terms of learning styles for user modeling and suggest that alternative characteristics or techniques might provide a more beneficial experience to users.

Ontologies and semantic web services are the basics of next generation semantic web. This upcoming technologies are useful in many fields such as bioinformatics, business collaboration, Data integration and etc. E-learning is also the field in which semantic web technologies can be used to provide dynamism in learning methodologies. E-learning includes set of tasks which may be instructional design, content development, authoring, delivery, assessment, feedback and etc. that can be sequenced and composed as workflow. Web based Learning Management Systems should concentrate on how to satisfy the e-learners requirements. In this paper we have suggested the theoretical framework ALMS-Adaptive Learning management System which focuses on three aspects 1) Extracting the knowledge from the use's interaction, behaviour and actions and translate them into semantics which are represented as Ontologies 2) Find the Learner style from the knowledge base and 3)deriving and composing the workflow depending upon the learner style. The intelligent agents are used in each module of the framework to perform reasoning and finally the personalized workflow for the e-learner has been recommended.