  For a product of interest, we propose a search method to surface a set of
reference products. The reference products can be used as candidates to support
downstream modeling tasks and business applications. The search method consists
of product representation learning and fingerprint-type vector searching. The
product catalog information is transformed into a high-quality embedding of low
dimensions via a novel attention auto-encoder neural network, and the embedding
is further coupled with a binary encoding vector for fast retrieval. We conduct
extensive experiments to evaluate the proposed method, and compare it with peer
services to demonstrate its advantage in terms of search return rate and
precision.
