23 citations · 200 across the 44 of their papers we have counts for
7 papers · 1 filter
Know What Not To Know: Users' Perception of Abstaining Classifiers
Andrea Papenmeier, Daniel Hienert, Yvonne Kammerer +2
Machine learning systems can help humans to make decisions by providing decision suggestions (i.e., a label for a datapoint). However, individual datapoints do not always provide e…
Evaluation of a Search Interface for Preference-Based Ranking -- Measuring User Satisfaction and System Performance
Dagmar Kern, Wilko van Hoek, Daniel Hienert
Finding a product online can be a challenging task for users. Faceted search interfaces, often in combination with recommenders, can support users in finding a product that fits th…
How Accurate Does It Feel? -- Human Perception of Different Types of Classification Mistakes
Andrea Papenmeier, Dagmar Kern, Daniel Hienert +2
Supervised machine learning utilizes large datasets, often with ground truth labels annotated by humans. While some data points are easy to classify, others are hard to classify, w…
UNDR: User-Needs-Driven Ranking of Products in E-Commerce
Andrea Papenmeier, Daniel Hienert, Firas Sabbah +2
Online retailers often offer a vast choice of products to their customers to filter and browse through. The order in which the products are listed depends on the ranking algorithm…
Dataset of Natural Language Queries for E-Commerce
Andrea Papenmeier, Dagmar Kern, Daniel Hienert +3
Shopping online is more and more frequent in our everyday life. For e-commerce search systems, understanding natural language coming through voice assistants, chatbots or from conv…
Starting Conversations with Search Engines -- Interfaces that Elicit Natural Language Queries
Andrea Papenmeier, Dagmar Kern, Daniel Hienert +3
Search systems on the Web rely on user input to generate relevant results. Since early information retrieval systems, users are trained to issue keyword searches and adapt to the l…