Publications by Dr.-Ing. Tim Donkers

Published in 2026

How Humans and LLMs Differ in Processing Uncertainty in Polarized Discourse

How Humans and LLMs Differ in Processing Uncertainty in Polarized Discourse

Published in 2025

Published in 2024

From explanations to human-AI co-evolution: Charting trajectories towards future user-centric AI

From explanations to human-AI co-evolution: Charting trajectories towards future user-centric AI

Published in 2023

An Instrument for measuring users’ meta-intents

Published in 2022

Meta-Intents in Conversational Recommender Systems

Meta-Intents in Conversational Recommender Systems

Published in 2021

The Dual Echo Chamber: Modeling Social Media Polarization for Interventional Recommending

Published in 2020

Leveraging Arguments in User Reviews for Generating and Explaining Recommendations

Effects of Argumentative Explanation Types on the Perception of Review-Based Recommendations

Explaining Recommendations by Means of Aspect-Based Transparent Memories

Published in 2019

Impact of Consuming Suggested Items on the Assessment of Recommendations in User Studies on Recommender Systems

Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems

Interactive Recommending with Tag-Enhanced Matrix Factorization (TagMF)

Published in 2018

Impact of Item Consumption on Assessment of Recommendations in User Studies

Argumentation-based explanations in recommender systems: Conceptual framework and empirical results

Trust-Related Effects of Expertise and Similarity Cues in Human-Generated Recommendations

Explaining Recommendations by Means of User Reviews

Published in 2017

Sequential User-based Recurrent Neural Network Recommendations

Published in 2016

Towards Understanding Latent Factors and User Profiles by Enhancing Matrix Factorization with Tags

Published in 2015

ConceptCloud -Entwicklung einer Applikation zur Unterstützung von Reflexionsprozessen im Online-Lernportal Go-Lab

Merging Latent Factors and Tags to Increase Interactive Control of Recommendations