The governance of recommender systems (RSs) in very large online platforms (VLOPs) is expected to change significantly under the Digital Services Act (DSA), which imposes specific obligations on algorithmic transparency and user control. However, beyond legal compliance, a critical question remains: How can recommender...
Sentiment information can enhance path-based explainable recommendation, yet advances are hindered by the lack of public benchmarks for sentiment-aware knowledge graph (SAKG) reasoning. We address this gap by systematically evaluating how different sentiment representation strategies and reward mechanisms affect utilit...
Alejandro Ariza-Casabona, Pol Pastells, Ludovico Boratto et al.· 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
Point-of-interest (POI) recommender systems typically optimize for accurate next-POI prediction, while personal safety considerations remain implicit or absent. In this demonstration, we present Lumi, a safety-aware POI recommendation system that integrates city-specific contextual features into an LLM-based recommenda...
Maria Alon Kutsaya, Maria Naddaf, Ludovico Boratto et al.· Proceedings of the 20th ACM...· 0 citations
Over the past two decades, Recommender Systems (RSs) have repeatedly changed how they integrate external knowledge, moving from constraints and side information to Knowledge Graphs (KG) and, more recently, Large Language Models (LLMs). This Past/Present/Future retrospective interprets that evolution as a sequence of re...
Claudio Pomo, Diego Baquero Sanz, Liam Claude Morris et al.· Proceedings of the 20th ACM...· 0 citations
A safety-aware next-POI recommendation method that leverages a Large Language Model (LLM) to generate predictions informed by both mobility patterns and crime-derived safety signals that substantially improves the safety profile of recommended POIs and surpasses state-of-the-art baselines in overall accuracy.
Rami Zaboura, Ludovico Boratto, Adir Solomon· Proceedings of the 20th ACM...· 0 citations
A reproducibility and replicability study that examines three major families of graph models, showing that strong classical methods remain highly competitive and that no single spectral model dominates across domains, and finds that spectral filtering often improves catalog exploration even when accuracy gains are marg...
Domenico de Gioia, Claudio Pomo, Ludovico Boratto et al.· Annual International ACM SIG...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
