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Explainable Neural News Recommender Systems
(Master thesis, 2021)De siste årene har bevitnet økt innsats forbundet med utviklingen av verktøy for å assistere brukere i å navigere nettbaserte underholdningstjenester gjennom anbefalingssystemer. Denne innsatsen er ikke ubegrunnet, da det ... -
Explainable reinforcement learning (XRL): a systematic literature review and taxonomy
(Journal article; Peer reviewed, 2023)In recent years, reinforcement learning (RL) systems have shown impressive performance and remarkable achievements. Many achievements can be attributed to combining RL with deep learning. However, those systems lack ... -
Explainable Research Paper Recommendation Using Scientific Knowledge Graphs
(Master thesis, 2021)Vitskapsfolk kan føle seg fortapte i søk etter relevant vitskapleg litteratur blant dei store mengdene som publiserast på dagleg basis. Målet til arXivDigest, som er ein teneste for anbefaling av vitskapleg litteratur, og ... -
Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study
(Chapter, 2022)Explainability in AI is becoming increasingly important as we delegate more safety-critical tasks to intelligent decision support systems. Case-Based Reasoning (CBR) systems are one way to build such systems. Understanding ... -
Explaining deep learning methods for recommender systems
(Master thesis, 2019)Systemkrav for anbefalingssystemer er i stadig endring. Formatet til anbefalinger, samt GDPR, har bragt med seg nye krav for anbefalingene som blir gitt. Forklarbarhet eller tolkbarhet er ett av disse nye kravene. orklarbarhet ... -
Explaining deep learning methods for recommender systems
(Master thesis, 2019)Systemkrav for anbefalingssystemer er i stadig endring. Formatet til anbefalinger, samt GDPR, har bragt med seg nye krav for anbefalingene som blir gitt. Forklarbarhet eller tolkbarhet er ett av disse nye kravene. Forklarbarhet ... -
Explaining fake news
(Master thesis, 2021)Dagens nyhetsbilde står forran en stor utfordring på grunn av mengden falske nyheter som florerer. Selv om en nyhetsartikkel kan bli automatisk detektert som falsk er det fremdeles vanskelig å forklare forskjellen mellom ... -
Explaining learning performance using response-time, self-regulation and satisfaction from content: an fsQCA approach
(Chapter, 2018)This study focuses on compiling students' response-time allocated to answer correctly or wrongly, their self-regulation, as well as their satisfaction from content, in order to explain high or medium/low learning performance. ... -
Explaining Traffic Situations – Architecture of a Virtual Driving Instructor
(Peer reviewed; Journal article, 2020)Intelligent tutoring systems become more and more common in assisting human learners. Distinct advantages of intelligent tutoring systems are personalized teaching tailored to each student, on-demand availability not ... -
Explaining travellers online information satisfaction: A complexity theory approach on information needs, barriers, sources and personal characteristics
(Journal article; Peer reviewed, 2017)This study explores the online information-seeking behaviour of travellers aspiring to accumulate travel-related information during their vacation planning. A theoretical model comprising information needs, ... -
Explaining User Experience in Mobile Gaming Applications: An fsQCA Approach
(Journal article; Peer reviewed, 2019)Purpose In the complex ecosystem of mobile applications multiple factors have been used to explain users’ behavior, without though focusing on how different combinations of variables may affect user behavior. The purpose ... -
Explaining your Neighbourhood: A CBR Approach for Explaining Black-Box Models
(Peer reviewed; Journal article, 2022) -
Explanation Methods in Clinical Decision Support: A Hybrid System Approach
(Master thesis, 2010)The use of computer-based decision support systems within the field of health science has over the last decades been extensively researched and tested, both in controlled environments and in clinical practice. Despite the ... -
Explanation-Aware Army Builder for Warhammer 40k
(Master thesis, 2016)The goal of this thesis is to design, implement and test the explanation-aware case base reasoning system for Warhammer 40k as described in the project of the same name. The system uses object oriented case base reasoning, ... -
Explanation-aware Case-based Reasoning
(Master thesis, 2011)When tasks traditionally performed by humans are automated it is important thatthe machines are able to communicate how these tasks are solved and why. Whena user is surprised by the point of time where the task is executed, ... -
Exploiting Latent Context for Effective Social Recommendation
(Doctoral theses at NTNU;2020:250, Doctoral thesis, 2020)With the rapid proliferation of online social networks, the information overload problem becomes increasingly severe, and recommender systems play a critical role in helping online users discover useful information matching ... -
Exploration of Automatic Quality Assurance of Code Reviews
(Master thesis, 2023)Denne masteroppgaven vil gå inn i forskningen omgående kvalitetssikringen av kode anmeldelser for å lage en effektiv måte for lærere og lærerassistenter til å vite kvaliteten av kode anmeldelser som studenter skriver. Med ... -
Exploration of human attitude towards social robots during simple non-digital game
(Master thesis, 2022)Introduksjonen av roboter i moderne sivilisasjon har endret måten maskiner fungerer på. De er ikke lenger i et herre-slave forhold til mennesket. Med introduksjonen av sosial robotikk kan disse kunstige vesenene nå stå på ... -
An Exploration on the Influence Factors of the Optimal Sample Width for Hyperspectral Remote Sensing Image Classification
(Peer reviewed; Journal article, 2023)Spectral-spatial classification of hyperspectral image (HSI) has made enormous achievements in many applications. One of the critical attributes that affects classification accuracy is the width of HSI cube/patch. To seek ... -
Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes
(Peer reviewed; Journal article, 2022)Background Patient-reported outcome measurements (PROMs) are commonly used in clinical practice to support clinical decision making. However, few studies have investigated machine learning methods for predicting PROMs ...