ABSTRACT

Product research in web shops involves categories as a helper system for search and browsing. The categorized offer reduces user’s mistakes and misled presentation. Hence, learning a product classifier to work with high precision and proper recall is of fundamental importance in order to provide high-quality shopping experience. However, products can be perfectly described they can miss users’ vocabulary when they do research. Thus, users’ content in form of search phrases has been utilized in order to extent a product data. Thus, original product names and classes have been supplemented with users’ search phrases, which changed classification accuracy in significantly positive direction but also slightly influencing on data consistency. The article presents new knowledge, research framework, machine learning classifiers selection and result analysis with implication for academia and some suggestions for business practitioners.

摘要

网上商店的产品研究涉及类别作为搜索和浏览的辅助系统。分类的报价减少了用户的错误和误导的演示。因此，学习产品分类器以实现高精度和适当的召回对于提供高质量的购物体验至关重要。然而，产品可以被完美地描述，他们在研究时可能会错过用户的词汇。因此，已经利用搜索短语形式的用户内容来扩展产品数据。因此，原始的产品名称和类别已经补充了用户的搜索词组，这在显着积极的方向改变了分类准确性，但对数据一致性也有轻微影响。文章介绍了新的知识、研究框架、