Word Embedding Quantization for Personalized Recommendation on Storage-Constrained Edge Devices in a Smart Store

Abstract

In recent years, word embedding models receive tremendous research attentions due to their capability of capturing textual semantics. This study investigates the issue of employing word embedding models into storage-constrained edge devices for personalized item-of-interest recommendation in a smart store. The challenge lies in that the existing embedding models are often too large to fit into a storage-constrained edge device. One naive idea is to reside the word embedding model in a secondary storage and process recommendation with that storage. However, this idea suffers from the burden of additional traffics. To this end, we propose a framework called Word Embedding Quantization (WEQ) which constructs an index upon a given word embedding model and stores the index in the primary storage to enable the use of the word embedding model by edge devices. One challenge for using the index is that the exact user profile is no longer ensured. However, we find that there are opportunities for computing the correct recommendation results by knowing only an inexact user profile. In this paper, we propose a series of techniques that leverage the opportunities for computing candidates with the goal of minimizing the accessing cost to a secondary storage in edge devices. Experiments are performed to verify the efficiency of the proposed techniques, demonstrating the feasibility of the proposed framework.