130 citations · 273 across the 19 of their papers we have counts for
9 papers · 1 filter
SPRIG: Semantic-ID-enhanced Paths for Knowledge Graph-based Generative Recommendation
Justin Hangoebl, Marta Moscati, Alessandro B. Melchiorre +2
Recommender systems leveraging generative models often generate item identifiers directly, rather than ranking catalog items by a recommendation score. Recent work extends beyond p…
SwapRec: Warming Up Cold Items Through Training-Time Swaps
Marta Moscati, Jan Malte Lichtenberg, Davide Abbattista +3
Interactions with cold items negatively impact real-time personalization of ID-based recommender systems. This is because the use of such interactions degrades user preference esti…
Adaptive Autoguidance for Item-Side Fairness in Diffusion Recommender Systems
Zihan Li, Gustavo Escobedo, Marta Moscati +2
Diffusion recommender systems achieve strong recommendation accuracy but often suffer from popularity bias, resulting in unequal item exposure. To address this shortcoming, we intr…
Single-Branch Network Architectures to Close the Modality Gap in Multimodal Recommendation
Christian Ganhör, Marta Moscati, Anna Hausberger +2
Traditional recommender systems rely on collaborative filtering, using past user-item interactions to help users discover new items in a vast collection. In cold start, i.e., when…
Parameter-Efficient Single Collaborative Branch for Recommendation
Marta Moscati, Shah Nawaz, Markus Schedl
Recommender Systems (RS) often rely on representations of users and items in a joint embedding space and on a similarity metric to compute relevance scores. In modern RS, the modul…
Familiarizing with Music: Discovery Patterns for Different Music Discovery Needs
Marta Moscati, Darius Afchar, Markus Schedl +1
Humans have the tendency to discover and explore. This natural tendency is reflected in data from streaming platforms as the amount of previously unknown content accessed by users.…
Unsupervised Graph Embeddings for Session-based Recommendation with Item Features
Andreas Peintner, Marta Moscati, Emilia Parada-Cabaleiro +2
In session-based recommender systems, predictions are based on the user's preceding behavior in the session. State-of-the-art sequential recommendation algorithms either use graph…
A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios
Christian Ganhör, Marta Moscati, Anna Hausberger +2
Most recommender systems adopt collaborative filtering (CF) and provide recommendations based on past collective interactions. Therefore, the performance of CF algorithms degrades…
Making Alice Appear Like Bob: A Probabilistic Preference Obfuscation Method For Implicit Feedback Recommendation Models
Gustavo Escobedo, Marta Moscati, Peter Muellner +4
Users' interaction or preference data used in recommender systems carry the risk of unintentionally revealing users' private attributes (e.g., gender or race). This risk becomes pa…