Analysis of Matric Product Matching Between Cosine Similarity with Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec in PT. Pricebook Digital Indonesia

Abstract

Consumers today have the option to purchase products from thousands of e-commerce. However, the completeness of the product specifications and taxonomies used to organize products differently in different electronic shop differently. To improve the consumer experience, Pricebook approach for integration of the product through the website to find the cheapest price from various platforms. In our writing, we do approach by using a model of neural language such as TF-IDF (term frequency-inverse document frequency) as well as Word2vec by using the method of cosine similarity. TF-IDF is a way to give the relationship a word weighting (term) against the document. Semantic vector or word embedding is one way to represent the structure of a sentence will be in align with manipulating sentences into vector shapes with Word2Vec. Cosine similarity method is a method to calculate the similarity between two objects that is expressed in two vectors by using keywords (keywords) of a document as the size so that it leads to more products matching good performance and categorization. In addition, we compare the results of the representation of the TF-IDF with Word2vec against a number of the data.