  Sequential recommendation leverages interaction sequences to predict
forthcoming user behaviors, crucial for crafting personalized recommendations.
However, the true preferences of a user are inherently complex and
high-dimensional, while the observed data is merely a simplified and
low-dimensional projection of the rich preferences, which often leads to
prevalent issues like data sparsity and inaccurate model training. To learn
true preferences from the sparse data, most existing works endeavor to
introduce some extra information or design some ingenious models. Although they
have shown to be effective, extra information usually increases the cost of
data collection, and complex models may result in difficulty in deployment.
Innovatively, we avoid the use of extra information or alterations to the
model; instead, we fill the transformation space between the observed data and
the underlying preferences with randomness. Specifically, we propose a novel
model-agnostic and highly generic framework for sequential recommendation
called sample enrichment via temporary operations on subsequences (SETO), which
temporarily and separately enriches the transformation space via sequence
enhancement operations with rationality constraints in training. The
transformation space not only exists in the process from input samples to
preferences but also in preferences to target samples. We highlight our SETO's
effectiveness and versatility over multiple representative and state-of-the-art
sequential recommendation models (including six single-domain sequential models
and two cross-domain sequential models) across multiple real-world datasets
(including three single-domain datasets, three cross-domain datasets and a
large-scale industry dataset).
