2 papers
cs.IR2025
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…
cs.LG2024
Simultaneous Unlearning of Multiple Protected User Attributes From Variational Autoencoder Recommenders Using Adversarial Training
Gustavo Escobedo, Christian Ganhör, Stefan Brandl +2
In widely used neural network-based collaborative filtering models, users' history logs are encoded into latent embeddings that represent the users' preferences. In this setting, t…