activity
20182026
most citedContext-aware explainable recommendations over knowledge graphs

2 citations · 6 across the 6 of their papers we have counts for

collaborators

8 papers

cs.IR2026

Trajectory-Based Recommender Systems as Control Systems

Eriam Schaffter, Ahmed Bounekkar, Elsa Negre

Recommender Systems (RS) are a key research domain and play an increasing role in our content-overwhelmed lives. In this paper, we explore Trajectory-Based Recommender Systems (TBR…

cs.HC20241 cited

Fuzzy synthetic method for evaluating explanations in recommender systems

Jinfeng Zhong, Elsa Negre

Recommender systems aim to help users find relevant items more quickly by providing personalized recommendations. Explanations in recommender systems help users understand why such…

cs.AI2024

When factorization meets argumentation: towards argumentative explanations

Jinfeng Zhong, Elsa Negre

Factorization-based models have gained popularity since the Netflix challenge {(2007)}. Since that, various factorization-based models have been developed and these models have bee…

cs.IR2024

Recognizing Similar Crises through the Application of Ontology-based Knowledge Mining

Ngoc Luyen Le, Marie-Hélène Abel, Elsa Negre

Recognizing and learning from similar crisis situations is crucial for the development of effective response strategies. This study addresses the challenge of identifying similarit…

cs.LG20232 cited

Context-aware feature attribution through argumentation

Jinfeng Zhong, Elsa Negre

Feature attribution is a fundamental task in both machine learning and data analysis, which involves determining the contribution of individual features or variables to a model's o…

cs.IR20232 cited

Context-aware explainable recommendations over knowledge graphs

Jinfeng Zhong, Elsa Negre

Knowledge graphs contain rich semantic relationships related to items and incorporating such semantic relationships into recommender systems helps to explore the latent connections…