AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations

TL;DR

AutoEmb is an automated framework that dynamically adjusts embedding sizes in deep learning recommender systems based on user/item popularity, improving flexibility and performance in streaming environments.

Contribution

It introduces an end-to-end differentiable AutoML framework that automatically determines optimal embedding dimensions according to popularity in streaming recommender systems.

Findings

Effective in adjusting embedding sizes dynamically

Improves recommendation accuracy in streaming settings

Demonstrates superiority over fixed-dimension methods

Abstract

Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e.g. user/item identifiers) and meaningfully transform them in the low-dimensional space. The majority of existing DLRSs empirically pre-define a fixed and unified dimension for all user/item embeddings. It is evident from recent researches that different embedding sizes are highly desired for different users/items according to their popularity. However, manually selecting embedding sizes in recommender systems can be very challenging due to the large number of users/items and the dynamic nature of their popularity. Thus, in this paper, we propose an AutoML based end-to-end framework (AutoEmb), which can enable various embedding dimensions according to the popularity in an automated and dynamic manner. To be specific, we first enhance a typical DLRS to allow various embedding dimensions; then we propose an end-to-end differentiable framework that can automatically select different embedding dimensions according to user/item popularity; finally we propose an AutoML based optimization algorithm in a streaming recommendation setting. The experimental results based on widely used benchmark datasets demonstrate the effectiveness of the AutoEmb framework.