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Found 4 papers, 2 papers with code
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?
So far, most NBR studies have focused on optimizing the accuracy of the recommendation, whereas optimizing for beyond-accuracy metrics, e. g., item fairness and diversity remains largely unexplored.
Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping
In next basket recommendation (NBR), it is useful to distinguish between repeat items, i. e., items that a user has consumed before, and explore items, i. e., items that a user has not consumed before.
A Simulation Environment and Reinforcement Learning Method for Waste Reduction
We frame inventory restocking as a new reinforcement learning task that exhibits stochastic behavior conditioned on the agent's actions, making the environment partially observable.
Distributional Reinforcement Learning
reinforcement-learning
+1
A Next Basket Recommendation Reality Check
We propose a set of metrics that measure the repeat/explore ratio and performance of NBR models.
