  Slate recommendation generates a list of items as a whole instead of ranking
each item individually, so as to better model the intra-list positional biases
and item relations. In order to deal with the enormous combinatorial space of
slates, recent work considers a generative solution so that a slate
distribution can be directly modeled. However, we observe that such approaches
-- despite their proved effectiveness in computer vision -- suffer from a
trade-off dilemma in recommender systems: when focusing on reconstruction, they
easily over-fit the data and hardly generate satisfactory recommendations; on
the other hand, when focusing on satisfying the user interests, they get
trapped in a few items and fail to cover the item variation in slates. In this
paper, we propose to enhance the accuracy-based evaluation with slate variation
metrics to estimate the stochastic behavior of generative models. We illustrate
that instead of reaching to one of the two undesirable extreme cases in the
dilemma, a valid generative solution resides in a narrow "elbow" region in
between. And we show that item perturbation can enforce slate variation and
mitigate the over-concentration of generated slates, which expand the "elbow"
performance to an easy-to-find region. We further propose to separate a pivot
selection phase from the generation process so that the model can apply
perturbation before generation. Empirical results show that this simple
modification can provide even better variance with the same level of accuracy
compared to post-generation perturbation methods.
