Search Results for author:
Found 5 papers, 3 papers with code
SARDINE: A Simulator for Automated Recommendation in Dynamic and Interactive Environments
Simulators can provide valuable insights for researchers and practitioners who wish to improve recommender systems, because they allow one to easily tweak the experimental setup in which recommender systems operate, and as a result lower the cost of identifying general trends and uncovering novel findings about the candidate methods.
Towards Validating Long-Term User Feedbacks in Interactive Recommendation Systems
In this work, we revisited experiments on IRS with review datasets and compared RL-based models with a simple reward model that greedily recommends the item with the highest one-step reward.
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning
To address this issue, we propose a novel URL framework that causally predicts future states while increasing the dimension of the latent manifold by decorrelating the features in the latent space.
DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games
To alleviate this problem, we propose DraftRec, a novel hierarchical model which recommends characters by considering each player's champion preferences and the interaction between the players.
MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation
Successful sequential recommendation systems rely on accurately capturing the user's short-term and long-term interest.
