11 citations · 13 across the 6 of their papers we have counts for
6 papers
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…
Music4All A+A: A Multimodal Dataset for Music Information Retrieval Tasks
Jonas Geiger, Marta Moscati, Shah Nawaz +1
Music is characterized by aspects related to different modalities, such as the audio signal, the lyrics, or the music video clips. This has motivated the development of multimodal…
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.…
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…