Dietmar Jannach

Methodological Issues in Recommender Systems Research (Extended Abstract)

2020

The development of continuously improved machine learning algorithms for personalized item ranking lies at the core of today's research in the area of recommender systems. Over the years, the research community has developed widely-agreed best …

A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data

2023 · arXiv (Cornell University)

Tourism is an important application domain for recommender systems. In this domain, recommender systems are for example tasked with providing personalized recommendations for transportation, accommodation, points-of-interest (POIs), etc. Among these tasks, in particular the problem …

Adaptation and Evaluation of Recommendations for Short-term Shopping Goals

2015

An essential characteristic in many e-commerce settings is that website visitors can have very specific short-term shopping goals when they browse the site. Relying solely on long-term user models that are pre-trained on historical data …

When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation

2017

Deep learning methods have led to substantial progress in various application fields of AI, and in recent years a number of proposals were made to improve recommender systems with artificial neural networks. For the problem …

Sequence-Aware Recommender Systems

2018 · ACM Computing Surveys

Recommender systems are one of the most successful applications of data mining and machine-learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each …

Are we really making much progress? A worrying analysis of recent neural recommendation approaches

2019

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 learning in general, it has, as a result, become difficult …

Performance comparison of neural and non-neural approaches to session-based recommendation

2019

The benefits of neural approaches are undisputed in many application areas. However, today's research practice in applied machine learning---where researchers often use a variety of baselines, datasets, and evaluation procedures---can make it difficult to understand …

A Survey on Conversational Recommender Systems

2021 · ACM Computing Surveys

Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users’ preferences are estimated based on past …