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Found 6 papers, 1 papers with code
Future Impact Decomposition in Request-level Recommendations
Furthermore, we show that a reward-based future decomposition strategy can better express the item-wise future impact and improve the recommendation accuracy in the long term.
Reinforcing User Retention in a Billion Scale Short Video Recommender System
In this paper, we choose reinforcement learning methods to optimize the retention as they are designed to maximize the long-term performance.
Two-Stage Constrained Actor-Critic for Short Video Recommendation
One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning.
Self-Adaptive Label Augmentation for Semi-supervised Few-shot Classification
A major novelty of SALA is the task-adaptive metric, which can learn the metric adaptively for different tasks in an end-to-end fashion.
Exploiting Global Semantic Similarities in Knowledge Graphs by Relational Prototype Entities
By enforcing the entities' embeddings close to their associated prototypes' embeddings, our approach can effectively encourage the global semantic similarities of entities -- that can be far away in the KG -- connected by the same relation.
Deconfounding Duration Bias in Watch-time Prediction for Video Recommendation
We employ a causal graph illuminating that duration is a confounding factor that concurrently affects video exposure and watch-time prediction -- the first effect on video causes the bias issue and should be eliminated, while the second effect on watch time originates from video intrinsic characteristics and should be preserved.
