Word2Vec Based Item Vector Learning from Purchase Data

Abstract

Word2Vec, a distributed representation method in natural language processing, is applied to purchase data in order to achieve item vector learning with low-computational cost. We perform an experiment with real POS data, and it validates how window size and dimension parameters and input purchase data format affect item vector learning. The experimental results suggest that learned item vectors within same category are located neighborhoods on the feature space under the following conditions: window size is as large as possible; dimension is more than 40; input data format is based on item variation.