464 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Graz University of TechnologyAT18 papers
- Stellenbosch UniversityZA18 papers
- Lakeside LabsAT11 papers
- TU WienAT11 papers
- Max Planck Institute for Dynamics of Complex Technical SystemsDE8 papers
- Politecnico di MilanoIT8 papers
- TU Dortmund UniversityDE8 papers
- University of ViennaAT8 papers
- Technical University of MunichDE7 papers
- Universität InnsbruckAT7 papers
- University of SalzburgAT7 papers
- University of UdineIT7 papers
31 papers · 1 filter
Controlled Personalization in Legacy Media Online Services: A Case Study in News Recommendation
Marlene Holzleitner, Stephan Leitner, Hanna Lind Jorgensen +3
Personalized news recommendations have become a standard feature of large news aggregation services, optimizing user engagement through automated content selection. In contrast, le…
Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search
Matteo Attimonelli, Alessandro De Bellis, Claudio Pomo +3
Pre-trained language models (PLMs) are widely used to derive semantic representations from item metadata in recommendation and search. In sequential recommendation, PLMs enhance ID…
Diffusion Recommender Models and the Illusion of Progress: A Concerning Study of Reproducibility and a Conceptual Mismatch
Michael Benigni, Maurizio Ferrari Dacrema, Dietmar Jannach
Countless new machine learning models are published every year and are reported to significantly advance the state-of-the-art in top-n recommendation. However, earlier reproducibil…
A Worrying Reproducibility Study of Intent-Aware Recommendation Models
Faisal Shehzad, Maurizio Ferrari Dacrema, Dietmar Jannach
Lately, we have observed a growing interest in intent-aware recommender systems (IARS). The promise of such systems is that they are capable of generating better recommendations by…
Recommender Systems for Good (RS4Good): Survey of Use Cases and a Call to Action for Research that Matters
Dietmar Jannach, Alan Said, Marko Tkalčič +1
In the area of recommender systems, the vast majority of research efforts is spent on developing increasingly sophisticated recommendation models, also using increasingly more comp…
Fashion Image-to-Image Translation for Complementary Item Retrieval
Matteo Attimonelli, Claudio Pomo, Dietmar Jannach +1
The increasing demand for online fashion retail has boosted research in fashion compatibility modeling and item retrieval, focusing on matching user queries (textual descriptions o…
The Importance of Causality in Decision Making: A Perspective on Recommender Systems
Emanuele Cavenaghi, Alessio Zanga, Fabio Stella +1
Causality is receiving increasing attention in the Recommendation Systems (RSs) community, which has realised that RSs could greatly benefit from causality to transform accurate pr…
A Survey on Intent-aware Recommender Systems
Dietmar Jannach, Markus Zanker
Many modern online services feature personalized recommendations. A central challenge when providing such recommendations is that the reason why an individual user accesses the ser…
Improving Sequential Recommendations with LLMs
Artun Boz, Wouter Zorgdrager, Zoe Kotti +5
The sequential recommendation problem has attracted considerable research attention in the past few years, leading to the rise of numerous recommendation models. In this work, we e…
Performance Comparison of Session-based Recommendation Algorithms based on GNNs
Faisal Shehzad, Dietmar Jannach
In session-based recommendation settings, a recommender system has no access to long-term user profiles and thus has to base its suggestions on the user interactions that are obser…
Leveraging Large Language Models for Sequential Recommendation
Jesse Harte, Wouter Zorgdrager, Panos Louridas +3
Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches.…
Economic Recommender Systems -- A Systematic Review
Alvise De Biasio, Nicolò Navarin, Dietmar Jannach
Many of today's online services provide personalized recommendations to their users. Such recommendations are typically designed to serve certain user needs, e.g., to quickly find…
A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data
Zehui Wang, Wolfram Höpken, Dietmar Jannach
Tourism is an important application domain for recommender systems. In this domain, recommender systems are for example tasked with providing personalized recommendations for trans…
A Survey on Popularity Bias in Recommender Systems
Anastasiia Klimashevskaia, Dietmar Jannach, Mehdi Elahi +1
Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long…
Semi-supervised Adversarial Learning for Complementary Item Recommendation
Koby Bibas, Oren Sar Shalom, Dietmar Jannach
Complementary item recommendations are a ubiquitous feature of modern e-commerce sites. Such recommendations are highly effective when they are based on collaborative signals like…
Evaluating Conversational Recommender Systems: A Landscape of Research
Dietmar Jannach
Conversational recommender systems aim to interactively support online users in their information search and decision-making processes in an intuitive way. With the latest advances…
Fairness in Recommender Systems: Research Landscape and Future Directions
Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogin +2
Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these syst…
Top-N Recommendation Algorithms: A Quest for the State-of-the-Art
Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia +2
Research on recommender systems algorithms, like other areas of applied machine learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of acc…
Towards Retrieval-based Conversational Recommendation
Ahtsham Manzoor, Dietmar Jannach
Conversational recommender systems have attracted immense attention recently. The most recent approaches rely on neural models trained on recorded dialogs between humans, implement…
KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting
Martin Stettinger, Trang Tran, Ingo Pribik +5
Existing e-learning environments primarily focus on the aspect of providing intuitive learning contents and to recommend learning units in a personalized fashion. The major focus o…
Session-aware Recommendation: A Surprising Quest for the State-of-the-art
Sara Latifi, Noemi Mauro, Dietmar Jannach
Recommender systems are designed to help users in situations of information overload. In recent years, we observed increased interest in session-based recommendation scenarios, whe…
Exploring Longitudinal Effects of Session-based Recommendations
Andres Ferraro, Dietmar Jannach, Xavier Serra
Session-based recommendation is a problem setting where the task of a recommender system is to make suitable item suggestions based only on a few observed user interactions in an o…
Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems
Maurizio Ferrari Dacrema, Federico Parroni, Paolo Cremonesi +1
In recent years, algorithm research in the area of recommender systems has shifted from matrix factorization techniques and their latent factor models to neural approaches. However…
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…