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Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation
First, since we concern the reward of a set of recommended items, we model the online recommendation as a contextual combinatorial bandit problem and define the reward of a recommended set.
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication
Thus, the global policy of the whole page could be sub-optimal.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Single-Layer Graph Convolutional Networks For Recommendation
The results verify that the proposed model outperforms existing GCN models considerably and yields up to a few orders of magnitude speedup in training, in terms of the recommendation performance.
Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests
A contextual bandit problem is studied in a highly non-stationary environment, which is ubiquitous in various recommender systems due to the time-varying interests of users.
