In multi-seller online marketplaces, centrally aggregating user interaction data to drive personalized recommendations often leads to cross-seller privacy leakage, which results in the potential reconstruction of sensitive preferences and unintended disclosure of sellers’ strategic signals. Privacy-preserving mechanisms that rely only on public, shareable signals can enable personalization in these settings by augmenting local marketplace feedback …
Author: borattoludovico
GNN’s FAME: Fairness-Aware MEssages for Graph Neural Networks
In graph-based prediction settings, standard message passing in Graph Neural Networks often propagates correlations between neighborhoods and sensitive attributes, which results in biased node representations and unfair classification outcomes. In-processing mechanisms that modulate messages using protected-attribute relationships can enable fairness-aware representation learning by attenuating bias amplification during aggregation, as instantiated in this study through fairness-aware …
Can Path-Based Explainable Recommendation Methods based on Knowledge Graphs Generalize for Personalized Education?
In personalized education platforms, explainable recommendation is often pursued by transferring knowledge-graph path reasoning methods from other domains, yet differences in educational data and evaluation practices can make these transfers misaligned and leave it unclear which methods remain reliable and why. Knowledge-graph reasoning can enable transparent, structure-aware personalization in this setting by producing recommendation paths …
Addressing Personalized Diversity in Eyewear Recommendation:a Lenskart Case Study
Relevance-driven ranking on e-commerce category pages often produces repetitive recommendation lists that constrain exploration and concentrate exposure on a narrow slice of the catalog. Personalized diversification mechanisms can enable user-dependent variety by tuning diversity pressure to signals of whether a user behaves like a generalist or a specialist, or by surfacing novelty through exploration-driven contextual …
GNNFairViz: Visual analysis for fairness in graph neural networks
Graph neural networks are increasingly used to make predictions on relational data in settings such as social and financial networks. Yet, assessing whether these models treat demographic groups comparably is difficult because bias can arise not only from node attributes but also from the graph structure that drives message passing. By introducing a model-agnostic visual …
A Comparative Analysis of Text-Based Explainable Recommender Systems
We reproduce and benchmark prominent text-based explainable recommender systems to test the recurring claim that hybrid retrieval-augmented approaches deliver the best overall balance between explanation quality and grounding. Yet, prior evidence is hard to compare because studies diverge in datasets, preprocessing, target explanation definitions, baselines, and evaluation metrics. Under a unified benchmark on three real-world …
Enhancing recommender systems with provider fairness through preference distribution awareness
Users in specific geographic areas often have distinct preferences regarding the provenance of the items they consume. However, current recommender systems fail to align these preferences with provider visibility, resulting in demographic inequities. By employing re-ranking, it is possible to achieve preference distribution-aware provider fairness, ensuring equitable recommendations with minimal trade-offs in effectiveness. Recommender systems …
Robust Privacy-Preserving Federated Item Ranking in Online Marketplaces: Exploiting Platform Reputation for Effective Aggregation
Centralized item ranking in online marketplaces compromises user privacy and is vulnerable to manipulation. The introduction of a federated, reputation-based ranking system preserves privacy, ensures fairness, and delivers robust and effective rankings. The growth of online marketplaces has transformed consumer experiences, offering diverse products aggregated from multiple sellers. However, the centralized nature of these platforms …
EDGE: A Conversational Interface driven by Large Language Models for Educational Knowledge Graphs Exploration
Navigating educational data is a growing challenge. EDGE offers a fusion of large language models and knowledge graphs to enable intuitive, natural language-driven exploration, empowering educators, learners, and administrators with actionable insights for accessing and understanding educational ecosystems. In an era where digital education platforms generate vast amounts of data, navigating and making sense of …
KGGLM: A Generative Language Model for Generalizable Knowledge Graph Representation Learning in Recommendation
Current recommender systems struggle to unify knowledge representation across tasks, leading to inefficiencies and reduced interpretability. KGGLM addresses this by leveraging generative language models for generalizable and task-adaptive knowledge graph learning, achieving state-of-the-art performance in both knowledge completion and recommendation. Recommender systems are central in personalizing user experiences across domains, from e-commerce to entertainment. A …