428 citations · 3.6k across the 40 of their papers we have counts for
9 papers · 1 filter
"The Squawk Bot": Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering
Xuan-Hong Dang, Syed Yousaf Shah, Petros Zerfos
Multimodal analysis that uses numerical time series and textual corpora as input data sources is becoming a promising approach, especially in the financial industry. However, the m…
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research
Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi +1
The design of algorithms that generate personalized ranked item lists is a central topic of research in the field of recommender systems. In the past few years, in particular, appr…
Empirical Analysis of Session-Based Recommendation Algorithms
Malte Ludewig, Noemi Mauro, Sara Latifi +1
Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session…
Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach
Deep learning techniques have become the method of choice for researchers working on algorithmic aspects of recommender systems. With the strongly increased interest in machine lea…
Measuring the Business Value of Recommender Systems
Dietmar Jannach, Michael Jugovac
Recommender Systems are nowadays successfully used by all major web sites (from e-commerce to social media) to filter content and make suggestions in a personalized way. Academic r…
On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems
Gabriel de Souza P. Moreira, Dietmar Jannach, Adilson Marques da Cunha
News recommender systems are designed to surface relevant information for online readers by personalizing their user experiences. A particular problem in that context is that onlin…
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke +5
Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tas…
Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
Gabriel de Souza Pereira Moreira, Dietmar Jannach, Adilson Marques da Cunha
Recommender systems help users deal with information overload by providing tailored item suggestions to them. The recommendation of news is often considered to be challenging, sinc…
Are Query-Based Ontology Debuggers Really Helping Knowledge Engineers?
Patrick Rodler, Dietmar Jannach, Konstantin Schekotihin +1
Real-world semantic or knowledge-based systems, e.g., in the biomedical domain, can become large and complex. Tool support for the localization and repair of faults within knowledg…