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

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.

# 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.