  Scaling of neural networks has recently shown great potential to improve the
model capacity in various fields. Specifically, model performance has a
power-law relationship with model size or data size, which provides important
guidance for the development of large-scale models. However, there is still
limited understanding on the scaling effect of user behavior models in
recommender systems, where the unique data characteristics (e.g. data scarcity
and sparsity) pose new challenges to explore the scaling effect in
recommendation tasks. In this work, we focus on investigating the scaling laws
in large sequential recommendation models. Specially, we consider a pure
ID-based task formulation, where the interaction history of a user is formatted
as a chronological sequence of item IDs. We don't incorporate any side
information (e.g. item text), because we would like to explore how scaling law
holds from the perspective of user behavior. With specially improved
strategies, we scale up the model size to 0.8B parameters, making it feasible
to explore the scaling effect in a diverse range of model sizes. As the major
findings, we empirically show that scaling law still holds for these trained
models, even in data-constrained scenarios. We then fit the curve for scaling
law, and successfully predict the test loss of the two largest tested model
scales. Furthermore, we examine the performance advantage of scaling effect on
five challenging recommendation tasks, considering the unique issues (e.g. cold
start, robustness, long-term preference) in recommender systems. We find that
scaling up the model size can greatly boost the performance on these
challenging tasks, which again verifies the benefits of large recommendation
models.
