Partner-based item recommendations are described. A computing
system receives historical data associated with
user engagement and transactions for a set of items. The computing
system selects a set of candidate items from the set of items based on a set of categories associated with the set of items, a set of popularity
metrics associated with the set of items, and a set of similarity
metrics associated with the set of candidate items and the set of items. The computing
system generates, for each candidate item, a respective user preference
score based on the historical data and using a neural network including an attention mechanism and a weighted
loss function. The computing system orders the set of candidate items according to the user preference scores and a category distribution for the set of items. The computing system outputs at least one candidate item based on the ordered set of candidate items.