Master Thesis

This repository contains the source for my Master Thesis "Product Taxonomy Matching in E-Commerce Environments". It was handed in on May 3rd, 2020 at the University of Mannheim.

Repository Layout

Thesis contains the LaTeX code to produce the written Thesis that was handed in at the Data and Web Science group at the University of Mannheim. You can use the build.sh script to compile the Thesis and output a thesis.pdf. It should work with any standard TeX distribution.

Code contains the source code to reproduce the experiments in this Thesis.

Requirements

• Python 3.x
• Maven 3.x
• Java 1.8 or later

Quick Start

mkdir -p Data
wget https://steffen911-papers.s3.eu-central-1.amazonaws.com/Data/products.json -O Data/products.json
wget https://steffen911-papers.s3.eu-central-1.amazonaws.com/Data/training.csv -O Data/training.csv
wget https://steffen911-papers.s3.eu-central-1.amazonaws.com/Data/goldstandard_positive.csv -O Data/goldstandard_positive.csv
pipenv install
pipenv shell
cd Code
python main.py

Code Documentation

The layout of the Code directory is as follows:

Code
├── crawler
├── main.py
├── matchers
├── thesis
└── wdc

main.py runs all sub-components at once. First, it creates pairs from the crawled datasets and creates a labelled training set from them. Then it runs the experiments on the manually annotated goldstandard.

The individual methods that are used from main.py are in the thesis package. There are methods to create the training set and run the experiments. It also contains a package to create statistics about the training set. To get running, simply download the following files and put it into a Data directory on the project root.

• clothing_products.json
• electronic_products.json
• products.json
• training.csv (Generated labels)
• goldstandard_positive.csv (Manually verified positive examples)

Afterwards run main.py and everything should run out of the box. All code expects to be executed from the Code directory.

In thesis/experiments.py different matchers are used on the goldstandard and training set. All matcher implementations are in the matchers package, except for S-Match were we reused the open source implementation provided by the authors. The source code can be found on GitHub. We also provide an updated version in this repository that fixes minor bugs.

The crawler package contains code to retrieve data from the PLDs we covered and wdc contains code to extract the categories from the WDC Product Corpus and combine them with the WDC training dataset for product matching.