  Recommender systems typically represent users and items by learning their
embeddings, which are usually set to uniform dimensions and dominate the model
parameters. However, real-world recommender systems often operate in streaming
recommendation scenarios, where the number of users and items continues to
grow, leading to substantial storage resource consumption for these embeddings.
Although a few methods attempt to mitigate this by employing embedding size
search strategies to assign different embedding dimensions in streaming
recommendations, they assume that the embedding size grows with the frequency
of users/items, which eventually still exceeds the predefined memory budget
over time. To address this issue, this paper proposes to learn Scalable
Lightweight Embeddings for streaming recommendation, called SCALL, which can
adaptively adjust the embedding sizes of users/items within a given memory
budget over time. Specifically, we propose to sample embedding sizes from a
probabilistic distribution, with the guarantee to meet any predefined memory
budget. By fixing the memory budget, the proposed embedding size sampling
strategy can increase and decrease the embedding sizes in accordance to the
frequency of the corresponding users or items. Furthermore, we develop a
reinforcement learning-based search paradigm that models each state with mean
pooling to keep the length of the state vectors fixed, invariant to the
changing number of users and items. As a result, the proposed method can
provide embedding sizes to unseen users and items. Comprehensive empirical
evaluations on two public datasets affirm the advantageous effectiveness of our
proposed method.
