  At the heart of contemporary recommender systems (RSs) are latent factor
models that provide quality recommendation experience to users. These models
use embedding vectors, which are typically of a uniform and fixed size, to
represent users and items. As the number of users and items continues to grow,
this design becomes inefficient and hard to scale. Recent lightweight embedding
methods have enabled different users and items to have diverse embedding sizes,
but are commonly subject to two major drawbacks. Firstly, they limit the
embedding size search to optimizing a heuristic balancing the recommendation
quality and the memory complexity, where the trade-off coefficient needs to be
manually tuned for every memory budget requested. The implicitly enforced
memory complexity term can even fail to cap the parameter usage, making the
resultant embedding table fail to meet the memory budget strictly. Secondly,
most solutions, especially reinforcement learning based ones derive and
optimize the embedding size for each each user/item on an instance-by-instance
basis, which impedes the search efficiency. In this paper, we propose Budgeted
Embedding Table (BET), a novel method that generates table-level actions (i.e.,
embedding sizes for all users and items) that is guaranteed to meet
pre-specified memory budgets. Furthermore, by leveraging a set-based action
formulation and engaging set representation learning, we present an innovative
action search strategy powered by an action fitness predictor that efficiently
evaluates each table-level action. Experiments have shown state-of-the-art
performance on two real-world datasets when BET is paired with three popular
recommender models under different memory budgets.
