SAGEA: Sparse Autoencoder-based Group Embeddings Aggregation for Fairness-Preserving Group Recommendations
Vít Koštejn, Ladislav Peška, Martin Spišák · 2025
Figure 1: Overview of SAGEA: The training phase (left) consists of: (a) training the ELSA collaborative autoencoder on individual user interactions to learn dense user embeddings, and (b) training a Top-𝑘 sparse autoencoder (SAE) to reconstruct these embeddings via a disentangled, sparse representation.The disentangled representation can be understood as a dictionary of activated concepts (such as preference for "Old music", "Love", "Dark humor", etc., as highlighted in the example).During inference (right), individual group members are encoded into sparse representations via the ELSA encoder E 𝐸𝐿𝑆𝐴 extended with the SAE encoder E 𝑆𝐴𝐸 .These are aggregated using a fairness-preserving strategy to form a group-level sparse embedding.The SAE decoder (D 𝑆𝐴𝐸 ) transforms this back into a dense embedding, which is then decoded by the ELSA decoder (D 𝐸𝐿𝑆𝐴 ) to generate group-wise relevance scores.