Multi-objective recommender systems (MORS) often combine diverse goals via linear scalarization, where objectives’ weights are being exposed as tunable hyperparameters for system optimization, or as interactive knobs supporting user control. However, we argue that such an approach creates an illusion of control, in whi...
Patrik Dokoupil, Ladislav Peška· Proceedings of the 20th ACM...· 0 citations
Provider fairness in recommender systems is commonly addressed through reranking, which assumes that the candidate set already contains sufficient items from under-exposed provider groups. We study a setting in which this assumption breaks down: catalog representation does not translate into recommendation exposure, an...
Patrik Dokoupil, L. Boratto, Ladislav Peška· Proceedings of the 20th ACM...· 0 citations
Pretrained text embeddings enable content-based cold-item recommendation, but their domain-agnostic objectives may underrepresent distinctions that matter within a particular recommendation domain. We investigate lightweight autoencoder adapters that specialize these embeddings without retraining or finetuning the embe...
Vojtěch Vančura, G. Medda, Martin Spišák et al.· Proceedings of the 20th ACM...· 0 citations
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