Comparing and matching product names from different stores/suppliers

Comparing and Matching Product Names from Different Stores/Suppliers

In e-commerce, it’s crucial to identify products that are the same even if they have slightly different names. This task is known as product name matching or normalization. This article will explore techniques for comparing and matching product names from different stores/suppliers.

Challenges of Product Name Matching

1. Variations in Naming Conventions

• Different brands use different naming conventions.
• Stores might add prefixes or suffixes to product names (e.g., “Our Brand – “, “Best Deal: “)
• Product names may include size, color, or other specifications in different ways.

2. Typos and Misspellings

• Human error can lead to typos and misspellings in product names.
• Variations in keyboard layouts can introduce errors.

3. Synonym and Near-Synonym Variations

• Different words can be used to describe the same product (e.g., “sneakers” vs. “athletic shoes”).
• Near-synonyms can also pose challenges (e.g., “large” vs. “extra large”).

Techniques for Product Name Matching

1. String Similarity Algorithms

These algorithms measure the similarity between two strings based on the number of common characters or substrings. Popular algorithms include:

• Levenshtein Distance
• Jaccard Similarity
• Cosine Similarity

Example: Levenshtein Distance

The Levenshtein distance calculates the minimum number of edits (insertions, deletions, or substitutions) required to transform one string into another.

from nltk.metrics import edit_distance

name1 = "Apple iPhone 13 Pro"
name2 = "Apple iPhone 13 Pro Max"

distance = edit_distance(name1, name2)

print(distance) # Output: 4

2. Tokenization and Stemming

Tokenization breaks down a string into individual words or units. Stemming reduces words to their root forms. These techniques help normalize product names and remove unnecessary variations.

Example: Tokenization and Stemming

import nltk
from nltk.stem import PorterStemmer

name = "Running Shoes - Size 10 - Blue"

tokens = nltk.word_tokenize(name)

stemmer = PorterStemmer()
stemmed_tokens = [stemmer.stem(token) for token in tokens]

print(stemmed_tokens) # Output: ['run', 'shoe', '-', 'size', '10', '-', 'blue']

3. Natural Language Processing (NLP)

NLP techniques can be used to understand the meaning of product names and identify similar products even when their names have different structures.

• Word Embeddings
• Semantic Similarity
• Machine Learning Models

4. Rule-Based Matching

Define specific rules to identify patterns in product names and match them accordingly. This approach can be helpful for handling common variations within specific categories.

Example: Rule-Based Matching

import re

name1 = "Nike Air Max 1"
name2 = "Nike Air Max 1 (White)"

if re.search(r"^Nike Air Max 1.*$", name2):
    print("Match!") # Output: Match!

Choosing the Right Approach

The best approach for product name matching depends on factors like:

• The complexity of your product catalog
• The level of accuracy required
• Available resources (data, computational power)

Often, a combination of techniques is used to achieve the best results.

Conclusion

Product name matching is an essential task for e-commerce businesses. By leveraging various techniques, you can ensure that similar products are identified and presented effectively to customers.