2019/04/11 by Chu Wang, Lei Tang, Wang, Chu +9
Computer Science · Mathematics · #Advanced Image and Video Retrieval Techniques #Image Retrieval and Classification Techniques #Web Data Mining and Analysis #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1904.05985
arxiv created 2019/04/11 · arxiv updated 2019/04/15
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.