# Skill 143

> Analyze and measure the similarity between different hierarchical category paths for e-commerce applications using advanced distance metrics and visualization techniques.

- Skill: `legendtkl/skill-143` (Agent Skill)
- Install (CLI): `npx skillmds@latest add legendtkl/skill-143`
- Raw SKILL.md: https://api.skillmd.com/api/skills/legendtkl/skill-143/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: legendtkl (https://skillmd.com/u/legendtkl)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/legendtkl/skill-143

---


# Taxonomy Path Similarity Analysis

Measure the similarity between hierarchical category paths and visualize the relationships to enhance product categorization and improve user experience.

## Problem

Given diverse category paths such as "electronics -> computers -> laptops" and "tech -> gadgets -> laptops", we want to analyze the similarity between these paths to understand their relations and overlap. This can help in identifying redundant categories and optimizing product listings. 

## Methodology

1. **Distance Metrics**: Use Jaccard similarity, cosine similarity, or Levenshtein distance to quantify the similarity between category paths.
2. **Visualization**: Generate visual representations such as dendrograms or heatmaps to showcase the similarity between paths.
3. **Threshold-Based Linking**: Establish thresholds for similarity scores to identify and merge closely related categories.

## Output

A DataFrame with columns:
- `path_1`: First category path
- `path_2`: Second category path
- `similarity_score`: Calculated similarity score between paths
- `is_similar`: Boolean flag indicating if paths are similar based on a threshold

## Installation

```bash
pip install pandas numpy scipy matplotlib seaborn
```

## 4-Step Pipeline

### Step 1: Load and Preprocess Paths (`step1_load_and_preprocess.py`)
- **Input**: List of category paths as strings.
- **Process**: Normalize paths, remove special characters, and convert to a standard format for analysis.
- **Output**: Cleaned list of category paths.

### Step 2: Compute Similarity Matrix (`step2_similarity_computation.py`)
- **Input**: Cleaned list of category paths.
- **Process**: Compute a similarity matrix using chosen distance metrics.
- **Output**: DataFrame containing the similarity scores for all pairs of paths.

### Step 3: Identify Similar Paths (`step3_path_merging.py`)
- **Input**: Similarity DataFrame from Step 2.
- **Process**: Apply thresholding to filter and merge similar paths.
- **Output**: DataFrame of merged paths with similarity scores.

### Step 4: Visualize Similarity (`step4_visualization.py`)
- **Input**: Merged DataFrame.
- **Process**: Create visualizations (heatmaps/dendrograms) to represent the similarity relationships.
- **Output**: Generated visual outputs for analysis.

```python
# Example of computing similarity
import pandas as pd
from sklearn.metrics import jaccard_score

# Assuming paths are preprocessed into a binary format for Jaccard
path_1 = [1, 1, 0, 1]
path_2 = [1, 0, 1, 1]

similarity = jaccard_score(path_1, path_2)
print(f"Jaccard Similarity: {similarity}")
```

This pipeline will help in effectively quantifying and visualizing taxonomy path similarities, aiding in better decision-making for category management in e-commerce.