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 …
Category: Recommender systems
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 …
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 …
AMBAR: A dataset for Assessing Multiple Beyond-Accuracy Recommenders
Recommender systems are a key tool for personalization in today’s digital age. They help us discover new music, books, or movies by predicting what we might like based on past interactions. But as recommender systems evolve, researchers and practitioners recognize that traditional metrics like accuracy alone aren’t enough. Factors like fairness, diversity, and user satisfaction …
Fair Augmentation for Graph Collaborative Filtering
While fairness in Graph Collaborative Filtering remains under-explored and often inconsistent across methodologies, targeted graph augmentation can effectively mitigate demographic biases while maintaining high recommendation utility. Fairness in recommender systems is not just an ethical challenge but a measurable, achievable goal. In a paper, in collaboration with Francesco Fabbri, Gianni Fenu, Mirko Marras, and Giacomo …
SM-RS: Single- and Multi-Objective Recommendations with Contextual Impressions and Beyond-Accuracy Propensity Scores
A recommender system is only as effective as its understanding of user propensities. The SM-RS dataset links contextual impressions with self-reported preferences, enabling the development of personalized, multi-objective recommendations. Recommender systems (RS) have long focused on delivering accurate results, aiming to align recommendations with user profiles. However, as user expectations evolve, beyond-accuracy metrics such as …
Unmasking Privacy: A Reproduction and Evaluation Study of Obfuscation-based Perturbation Techniques for Collaborative Filtering
A well-designed obfuscation framework can significantly enhance user privacy in recommender systems without fundamentally compromising their performance, offering a viable path to balancing personalization and privacy. As digital platforms increasingly rely on personalization to engage users, recommender systems have become a central component of e-commerce and entertainment industries. However, this personalization often comes at the …
Bringing Equity to Coarse and Fine-Grained Provider Groups in Recommender Systems
Achieving true fairness in recommender systems requires moving beyond broad demographic categories to address disparities at a fine-grained level, ensuring equitable representation for all subgroups. This goal can be made feasible through advanced re-ranking methodologies like CONFIGRE. Recommender systems are ubiquitous in today’s digital landscape, providing tailored suggestions to users in domains like e-commerce, entertainment, …
User Perceptions of Diversity in Recommender Systems
Understanding user perceptions of diversity in recommender systems reveals a paradox: while users favor intuitive, metadata-driven metrics like genres, their ability to distinguish finer variations in diversity is limited, highlighting the need for user-aligned algorithms that balance diversity with relevance. In this study, in collaboration with Patrik Dokoupil and Ladislav Peska, and published in the …