Search Results for author:
Found 3 papers, 2 papers with code
Enhanced Generative Recommendation via Content and Collaboration Integration
However, existing generative recommendation approaches still encounter challenges in (i) effectively integrating user-item collaborative signals and item content information within a unified generative framework, and (ii) executing an efficient alignment between content information and collaborative signals.
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure
Then, we introduce a penalty to items with high exposure probability to avoid the overestimation of user preference for biased samples.
Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems
For the second issue, we propose introducing contrastive signals between augmented states and the state randomly sampled from other sessions to improve the state representation learning further.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
