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With the continuous increase of users and items, conventional recommendersystems trained on static datasets can hardly adapt to changing environments.The high-throughput data requires the model to be updated in a timely mannerfor capturing the user interest dynamics, which leads to the emergence ofstreaming recommender systems. Due to the prevalence of deep learning-basedrecommender systems, the embedding layer is widely adopted to represent thecharacteristics of users, items, and other features in low-dimensional vectors.However, it has been proved that setting an identical and static embedding sizeis sub-optimal in terms of recommendation performance and memory cost,especially for streaming recommendations. To tackle this problem, we firstrethink the streaming model update process and model the dynamic embedding sizesearch as a bandit problem. Then, we analyze and quantify the factors thatinfluence the optimal embedding sizes from the statistics perspective. Based onthis, we propose the \textbf{D}ynamic \textbf{E}mbedding \textbf{S}ize\textbf{S}earch (\textbf{DESS}) method to minimize the embedding size selectionregret on both user and item sides in a non-stationary manner. Theoretically,we obtain a sublinear regret upper bound superior to previous methods.Empirical results across two recommendation tasks on four public datasets alsodemonstrate that our approach can achieve better streaming recommendationperformance with lower memory cost and higher time efficiency.

Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System