Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Hienert, Daniel"'
Publikováno v:
Proceedings of Mensch und Computer 2024 (MuC '24). ACM, New York, NY, USA, 129-139
Recent advances in natural language processing and deep learning have accelerated the development of digital assistants. In conversational commerce, these assistants help customers find suitable products in online shops through natural language conve
Externí odkaz:
http://arxiv.org/abs/2410.01291
Publikováno v:
In Companion Publication of the 2023 ACM Designing Interactive Systems Conference (DIS 2023 Companion)
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 enough clear evidence to make confident suggestions. Although methods e
Externí odkaz:
http://arxiv.org/abs/2309.05443
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 their preferences. However, those preferences are not always equally wei
Externí odkaz:
http://arxiv.org/abs/2302.06440
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, which reduces the inter-annotator agreement. This causes noise for the
Externí odkaz:
http://arxiv.org/abs/2302.06413
Publikováno v:
In Proceedings of the SIGIR 2022 eCom workshop
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 employed in the online shop. State-of-the-art ranking methods are comp
Externí odkaz:
http://arxiv.org/abs/2302.06398
Publikováno v:
In CHIIR '21: Proceedings of the 2021 Conference on Human Information Interaction and Retrieval
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 conversational search is an essential ability to understand what the user
Externí odkaz:
http://arxiv.org/abs/2302.06355
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 language of the system. Recent research has shown that users often with
Externí odkaz:
http://arxiv.org/abs/2302.06349
Publikováno v:
In Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2022, edited by Stefania Degaetano, Anna Kazantseva, Nils Reiter, and Stan Szpakowicz, 1-6
Word embeddings are an essential instrument in many NLP tasks. Most available resources are trained on general language from Web corpora or Wikipedia dumps. However, word embeddings for domain-specific language are rare, in particular for the social
Externí odkaz:
http://arxiv.org/abs/2302.06174
Autor:
Breuer, Timo, Tavakolpoursaleh, Narges, Schaible, Johann, Hienert, Daniel, Schaer, Philipp, Castro, Leyla Jael
Publikováno v:
Information Retrieval Meeting (IRM 2022)
Involving users in early phases of software development has become a common strategy as it enables developers to consider user needs from the beginning. Once a system is in production, new opportunities to observe, evaluate and learn from users emerg
Externí odkaz:
http://arxiv.org/abs/2210.13202
Autor:
Otto, Christian, Rokicki, Markus, Pardi, Georg, Gritz, Wolfgang, Hienert, Daniel, Yu, Ran, von Hoyer, Johannes, Hoppe, Anett, Dietze, Stefan, Holtz, Peter, Kammerer, Yvonne, Ewerth, Ralph
The emerging research field Search as Learning investigates how the Web facilitates learning through modern information retrieval systems. SAL research requires significant amounts of data that capture both search behavior of users and their acquired
Externí odkaz:
http://arxiv.org/abs/2201.02339