Towards Classifying HTML-embedded Product Data Based On Machine Learning Approach - Matveiev, Oleksandr; Zubenko, Anastasiia; Yevtushenko, Dmitry; Cherednichenko, Olga (My University, 2021)
In this paper we explored machine learning approaches using descriptions and titles to classify footwear by brand. The provided data were taken from many different online stores. In particular, we have created a pipeline that automatically classifies product brands based on the provided data. The dataset is provided in JSON format and contains more than 40,000 rows. The categorization component was implemented using K-Nearest Neighbour (K-NN) and Support Vector Machine (SVM) algorithms. The results of the pipeline construction were evaluated basing on the classification report, especially the Precision weighted average value was considered during the calculation, which reached 79.0% for SVM and 72.0% for K-NN.
Keywords: product classification; SVM; K-Nearest Neighbour; TF-IDF; machine learning; vectorization; item matching