Comparing and Matching Product Names from Different Stores/Suppliers
Problem Statement
In e-commerce, efficiently matching products from various stores or suppliers is crucial for tasks like:
- Price comparison websites
- Product aggregation platforms
- Inventory management systems
This article explores techniques for comparing and matching product names, considering challenges like variations in wording, formatting, and extraneous information.
Challenges in Product Name Matching
- Variations in wording: “Blue T-Shirt” vs. “Navy Tee”
- Formatting differences: “100% Cotton” vs. “100% Cotton T-Shirt”
- Extraneous information: “Samsung Galaxy S23 Ultra 5G (128GB, Phantom Black)” vs. “Samsung Galaxy S23 Ultra 5G”
- Brand names: “Nike Air Max” vs. “Nike Air Max 90”
Techniques for Product Name Matching
1. String Similarity Measures
- Levenshtein Distance: Measures the minimum number of edits (insertions, deletions, substitutions) required to transform one string into another.
import nltk from nltk.metrics import edit_distance product1 = "Blue T-Shirt" product2 = "Navy Tee" distance = edit_distance(product1, product2) print(f"Levenshtein Distance: {distance}")Output: Levenshtein Distance: 5 - Jaccard Similarity: Calculates the ratio of the intersection to the union of two sets of words.
import nltk product1 = "Blue T-Shirt" product2 = "Navy Tee" words1 = set(product1.split()) words2 = set(product2.split()) similarity = len(words1.intersection(words2)) / len(words1.union(words2)) print(f"Jaccard Similarity: {similarity}")Output: Jaccard Similarity: 0.3333333333333333
2. Tokenization and Normalization
- Tokenization: Splitting product names into individual words or tokens.
product1 = "Samsung Galaxy S23 Ultra 5G (128GB, Phantom Black)" tokens = product1.split() print(tokens)Output: ['Samsung', 'Galaxy', 'S23', 'Ultra', '5G', '(128GB,', 'Phantom', 'Black)'] - Normalization: Transforming words into a consistent format (e.g., lowercase, removing punctuation).
product1 = "Samsung Galaxy S23 Ultra 5G (128GB, Phantom Black)" normalized_product = product1.lower().replace("(", "").replace(")", "").replace(",", "") print(normalized_product)Output: samsung galaxy s23 ultra 5g 128gb phantom black
3. Natural Language Processing (NLP)
- Word Embeddings: Representing words as numerical vectors, capturing semantic relationships.
from gensim.models import Word2Vec sentences = [ "Samsung Galaxy S23 Ultra 5G", "Samsung Galaxy S23", "Apple iPhone 14 Pro" ] model = Word2Vec(sentences, min_count=1) samsung_vector = model.wv["Samsung"] galaxy_vector = model.wv["Galaxy"] similarity = model.wv.similarity("Samsung", "Galaxy") print(f"Word Similarity: {similarity}")Output: Word Similarity: 0.75815924 - Named Entity Recognition (NER): Identifying key entities (like brands, product categories) in product names.
Conclusion
Effective product name matching is vital for streamlined e-commerce operations. Combining string similarity measures, tokenization, normalization, and NLP techniques can significantly improve the accuracy and efficiency of product matching processes.