Personalized Item-of-Interest Recommendation on Storage Constrained Smartphone Based on Word Embedding Quantization

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 resource-limited smartphones for personalized item recommendation. The challenge lies in that the existing embedding models are often too large to fit into a resource-limited smartphones. One naive idea is to incorporate a secondary storage by residing the model in the secondary storage and processing recommendation with the secondary storage. However, this idea suffers from the burden of additional traffics. To this end, we propose a framework called Word Embedding Quantization (WEQ) that constructs an index upon a given word embedding model and stores the index on the primary storage to enable the use of the word embedding model on smartphones. 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 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. Experiments are made to verify the efficiency of the proposed techniques, which demonstrates the feasibility of the proposed framework.