26 citations · 64 across the 19 of their papers we have counts for
6 papers · 1 filter
Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation
Clémence Réda, Tomas Rigaux, Hiba Bederina +3
A core research question in recommender systems is to propose batches of highly relevant and diverse items, that is, items personalized to the user's preferences, but which also mi…
Diversified recommendations of cultural activities with personalized determinantal point processes
Carole Ibrahim, Hiba Bederina, Daniel Cuesta +3
While optimizing recommendation systems for user engagement is a well-established practice, effectively diversifying recommendations without negatively impacting core business metr…
Bayesian-Guided Diversity in Sequential Sampling for Recommender Systems
Hiba Bederina, Jill-Jênn Vie
The challenge of balancing user relevance and content diversity in recommender systems is increasingly critical amid growing concerns about content homogeneity and reduced user eng…
Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
Jill-Jênn Vie, Hisashi Kashima
Knowledge tracing is a sequence prediction problem where the goal is to predict the outcomes of students over questions as they are interacting with a learning platform. By trackin…
Deep Factorization Machines for Knowledge Tracing
Jill-Jênn Vie
This paper introduces our solution to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We used deep factorization machines, a wide and deep learning mo…
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario
Jill-Jênn Vie, Florian Yger, Ryan Lahfa +4
Item cold-start is a classical issue in recommender systems that affects anime and manga recommendations as well. This problem can be framed as follows: how to predict whether a us…