25 citations · 77 across the 7 of their papers we have counts for
7 papers
Rethinking Group Recommender Systems in the Era of Generative AI: From One-Shot Recommendations to Agentic Group Decision Support
Dietmar Jannach, Amra Delić, Francesco Ricci +1
More than twenty-five years ago, first ideas were developed on how to design a system that can provide recommendations to groups of users instead of individual users. Since then, a…
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…
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…
Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases
Gabriele Sottocornola, Fabio Stella, Markus Zanker
Post-harvest diseases of apple are one of the major issues in the economical sector of apple production, causing severe economical losses to producers. Thus, we developed DSSApple,…
Personalised novel and explainable matrix factorisation
Ludovik Coba, Panagiotis Symeonidis, Markus Zanker
Recommendation systems personalise suggestions to individuals to help them in their decision making and exploration tasks. In the ideal case, these recommendations, besides of bein…
Decision Making of Maximizers and Satisficers Based on Collaborative Explanations
Ludovik Coba, Markus Zanker, Laurens Rook +1
Rating-based summary statistics are ubiquitous in e-commerce, and often are crucial components in personalized recommendation mechanisms. Largely left unexplored, however, is the i…
Exploring Users' Perception of Collaborative Explanation Styles
Ludovik Coba, Markus Zanker, Laurens Rook +1
Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. In this study we explore how users value differe…