Product Classification in E-Commerce using Distributional Semantics
This work addresses the problem of efficient and accurate product classification for e-commerce platforms, representing an incremental improvement over existing approaches.
The paper tackles product classification in e-commerce by proposing a new distributional semantics representation for document vectors and a two-level ensemble approach with path-wise, node-wise, and depth-wise classifiers, achieving better results on various evaluation metrics compared to earlier methods.
Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction. To address the above challenges, we propose a new distributional semantics representation for document vector formation. We also develop a new two-level ensemble approach utilizing (with respect to the taxonomy tree) a path-wise, node-wise and depth-wise classifiers for error reduction in the final product classification. Our experiments show the effectiveness of the distributional representation and the ensemble approach on data sets from a leading e-commerce platform and achieve better results on various evaluation metrics compared to earlier approaches.