Ludovico Boratto

From Side Information and Knowledge Graphs to Large Language Models: Two Decades of Knowledge Integration in Recommender Systems

It is argued that LLMs should be understood not as substitutes for structured knowledge, but as a new interaction layer that requires explicit grounding, and this retrospective interprets that evolution as a sequence of representational translations rather than simple replacements.

Lumi: An LLM-Grounded System for Safety-Aware POI Recommendations

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...

Safety-Aware Next-POI Recommendation with Large Language Models

Point of Interest (POI) recommendation has become a core task in location-based services, with modern systems increasingly driven by deep learning models that achieve strong predictive accuracy. Yet, despite these advances, most approaches optimize primarily for relevance, giving limited attention to an equally importa...

Assessing Sentiment Semantics in KG-Based Explainable Recommender Systems

It is revealed that sentiment-aware methods improve accuracy and diversity but reduce provider fairness, exposing a consistent trade-off, and sentiment path consistency rewards enhance recommendation quality and explanation coherence, whereas terminal-only sentiment constraints improve fairness at modest utility cost.

From Auditing to (Re)designing: Recommender Systems for User Empowerment in Very Large Online Platforms Under the Digital Services Act

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...

Assessing Sentiment Semantics in KG-Based Explainable Recommender Systems

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...

Candidate Retrieval for Provider Fairness under Severe Recommendation Imbalance

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...

Lumi: An LLM-Grounded System for Safety-Aware POI Recommendations

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...

From Side Information and Knowledge Graphs to Large Language Models: Two Decades of Knowledge Integration in Recommender Systems

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...

Safety-Aware Next-POI Recommendation with Large Language Models

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.

Explainable course recommendation with knowledge graphs: a comparative audit of diverse modeling paradigms

Mind the Metric: Reproducibility and Fair Benchmarking of Spectral Graph Models for Collaborative Filtering

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...