Can user models be learned at all? Inherent problems in machine learning for user modelling

2004 ◽  
Vol 19 (1) ◽  
pp. 61-88 ◽  
Author(s):  
MARTIN E. MÜLLER
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
User Model ◽  
User Modelling ◽  
Machine Learning Algorithms ◽  
Learning Problem ◽  
User Models ◽  
Modelling Techniques ◽  
Modelling Problem

Machine learning seems to offer the solution to many problems in user modelling. However, one tends to run into similar problems each time one tries to apply out-of-the-box solutions to machine learning. This article closely relates the user modelling problem to the machine learning problem. It explicates some inherent dilemmas that are likely to be overlooked when applying machine learning algorithms in user modelling. Some examples illustrate how specific approaches deliver satisfying results and discuss underlying assumptions on the domain or how learned hypotheses relate to the requirements on the user model. Finally, some new or underestimated approaches offering promising perspectives in combined systems are discussed. The article concludes with a tentative ‘‘checklist” that one might like to consider when planning to apply machine learning to user modelling techniques.

A Brief Survey on User Modelling in Human Computer Interaction

2012 ◽  
pp. 1-19
Author(s):  
Pradipta Biswas
Keyword(s):  
Human Computer Interaction ◽  
User Model ◽  
User Modelling ◽  
User Models ◽  
History Of Development ◽  
Wide Range ◽  
History Of ◽  
Modelling Techniques ◽  
Computer Interaction ◽  
Modelling Approaches

This chapter presents a brief survey of different user modelling techniques used in human computer interaction. It investigates history of development of user modelling techniques and classified the existing models into different categories. In the context of existing modelling approaches it presents a new user model and its deployment through a simulator to help designers in developing accessible systems for people with a wide range of abilities. This chapter will help system analysts and developers to select and use appropriate type of user models for their applications.

scholarly journals Provable Advantage in Quantum Phase Learning via Quantum Kernel Alphatron

2022 ◽  
Author(s):  
Wu Yusen ◽  
Bujiao Wu ◽  
Jingbo Wang ◽  
Xiao Yuan
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Quantum Phase ◽  
Learning Problems ◽  
Learning Problem ◽  
Quantum Phases ◽  
Speed Up ◽  
Efficient Prediction ◽  
Symmetry Protected

Abstract The use of quantum computation to speed-up machine learning algorithms is among the most exciting prospective applications in the NISQ era. Here, we focus on the quantum phase learning problem, which is crucially important in understanding many-particle quantum systems. We prove that, under widely believed complexity theory assumptions, quantum phase learning problem cannot be efficiently solved by machine learning algorithms using classical resources and classical data. Whereas using quantum data, we prove the universality of quantum kernel Alphatron in efficiently predicting quantum phases, indicating clear quantum advantages in such learning problems. We numerically benchmark the algorithm for a variety of problems, including recognizing symmetry-protected topological phases and symmetry-broken phases. Our results highlight the capability of quantum machine learning in efficient prediction of quantum phases of many-particle systems.

scholarly journals A survey of Emerging Techniques in Detecting SMS Spam

2019 ◽  
Vol 7 (5) ◽  
pp. 23-35
Author(s):  
Sahar Alqahtani ◽  
Daniyal Alghazzawi
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Mobile Users ◽  
Topic Modelling ◽  
The Past ◽  
Machine Learning Methods ◽  
Effective Performance ◽  
Modelling Techniques

In the past years, spammers have focused their attention on sending spam through short messages services (SMS) to mobile users. They have had some success because of the lack of appropriate tools to deal with this issue. This paper is dedicated to review and study the relative strengths of various emerging technologies to detect spam messages sent to mobile devices. Machine Learning methods and topic modelling techniques have been remarkably effective in classifying spam SMS. Detecting SMS spam suffers from a lack of the availability of SMS dataset and a few numbers of features in SMS. Various features extracted and dataset used by the researchers with some related issues also discussed. The most important measurements used by the researchers to evaluate the performance of these techniques were based on their recall, precision, accuracies and CAP Curve. In this review, the performance achieved by machine learning algorithms was compared, and we found that Naive Bayes and SVM produce effective performance.

A Brief Survey on User Modelling in Human Computer Interaction

2013 ◽  
pp. 102-119
Author(s):  
Pradipta Biswas
Keyword(s):  
Human Computer Interaction ◽  
User Model ◽  
User Modelling ◽  
User Models ◽  
History Of Development ◽  
Wide Range ◽  
History Of ◽  
Modelling Techniques ◽  
Computer Interaction ◽  
Modelling Approaches

This chapter presents a brief survey of different user modelling techniques used in human computer interaction. It investigates history of development of user modelling techniques and classified the existing models into different categories. In the context of existing modelling approaches it presents a new user model and its deployment through a simulator to help designers in developing accessible systems for people with a wide range of abilities. This chapter will help system analysts and developers to select and use appropriate type of user models for their applications.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

1290-P: Gut Microbiota in New-Onset Pediatric Patients with Type 1 Diabetes: Machine Learning Algorithms to Classify Microorganisms Disease-Linked

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1290-P
Author(s):  
GIUSEPPE D’ANNUNZIO ◽  
ROBERTO BIASSONI ◽  
MARGHERITA SQUILLARIO ◽  
ELISABETTA UGOLOTTI ◽  
ANNALISA BARLA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 1 Diabetes ◽  
Gut Microbiota ◽  
Pediatric Patients ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
New Onset
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