Lazy loaded image
Mathematics
Lazy loaded imageWeighted Euclidean Distance
Words 154Read Time 1 min
Jul 2, 2021
Apr 22, 2025
type
status
date
slug
summary
tags
category
icon
password

Calculating Product Similarity Using Weighted Euclidean Distance

Suppose we have two products, Product A and Product B, and each has the following features:
Feature Type
Feature Name
Product A
Product B
Weight (wᵢ)
Image Feature
Image Vector
0.5
0.7
0.4
Text Feature
Title Vector
0.8
0.6
0.3
Categorical
Brand (One-hot)
1.0
0.0
0.2
Categorical
Color (One-hot)
1.0
1.0
0.1

✅ Step-by-Step Calculation

We'll use the formula:

1. Image Feature:

2. Title/Text Feature:

3. Brand (One-hot):

4. Color (One-hot):


Final Distance:


Interpretation:

  • The smaller the distance, the more similar the products are.
  • In this example, the brand difference contributed the most to the distance due to its high weight and binary difference.
  • The image and text also influenced similarity moderately.
  • Color was identical, so it had no effect on the distance.
上一篇
SQL Q&A4 The JOIN operation
下一篇
Cosine Similarity