Product Grouping in Noisy E-commerce Datasets
Hybrid Approaches to Product Grouping in Noisy E-commerce Datasets: A Comparative Analysis of Set-Theoretic vs. Vector Space Models. In scientific literature, this problem is formally known as Short Text Clustering (STC) or Product Entity Resolution. The product similarity measures can be read from here: Measuring product similarity – 5 important secrets of python programming. Machine learning of product clustering you can find here: Hierarchical Agglomerative Clustering for Product Grouping.
The “Marketplace Deduplication” Problem
Context: Large marketplaces (Amazon, eBay, Alibaba, or a niche aggregator) allow thousands of third-party sellers to upload their own product feeds. Product Grouping for E-commerce Datasets indeed.
The Problem:
- Seller A uploads: “Apple iPhone 13, 128GB, Midnight”
- Seller B uploads: “iPhone 13 128 GB Black Unlocked”
- Seller C uploads: “Smartfon Apple iPhone 13 128GB (MLPF3PM/A)”
Why it’s hard – The Scientific Challenge
- Missing Identifiers: Sellers often omit EAN/UPC codes to avoid price comparisons.
- Attribute Noise: “Midnight” vs. “Black” (synonyms).
- Goal: You must cluster these into a single catalog entry (The “Golden Record”) to show the user one product page with a list of 3 sellers, rather than 3 separate search results.
- ** Metric:** False Positives are costly here (grouping an iPhone 13 Pro with a regular iPhone 13 causes returns).
The “Omnichannel Customer Stitching” Problem (Single Customer View)
Context: A retailer sells through a Website, a Mobile App, and Physical Stores. They want to know if the person browsing the app is the same person buying in the store.
The Problem:
- Record A (Online):
email: [email protected],cookie_id: xyz123,behavior: viewed running shoes - Record B (In-Store POS):
card_hash: ****-1234,loyalty_id: 998877,name: John Smith - Record C (Customer Support):
phone: +48 500...,name: Johnny Smith,complaint: "Shoes size 42 too small"
Why it’s hard:
Why it’s hard
- Disjoint Attributes: Record A has no phone number; Record C has no email. You need “transitive linking” (A links to B, B links to C $\rightarrow$ A links to C).
- Privacy/Hashing: You are often matching hashed values or partial PII (Personally Identifiable Information).
- Goal: Create a Customer 360 profile to send a targeted email: “Hi John, sorry the size 42 didn’t fit. Here is a discount for size 43.”
The “Competitor Price Monitoring” Problem
Context: An e-commerce store wants to automatically adjust their prices to be $1 cheaper than their biggest competitor.
The Problem:
- Your Product: “Samsung Galaxy S20 FE 5G Cloud Navy”
- Competitor Site: “Samsung S20 Fan Edition (Navy) – 5G Compatible”
Why it’s hard:
Why it’s hard – The Scientific Challenge:
- Adversarial Data: Competitors intentionally slightly alter names or use unique internal SKUs to prevent scraping and matching.
- Asymmetry: You have your full database (structured), but the competitor data is scraped (unstructured, noisy HTML).
- Goal: Map competitor SKUs to your SKUs with high precision. If you map to the wrong product (e.g., a cheaper “Lite” version), your dynamic pricing algorithm will lower your price too much and you lose money.
Abstract
- Problem: E-commerce catalogs suffer from redundancy (same product, different sizes/variants).
- Gap: Manual grouping is impossible; Deep Learning is overkill/imprecise for strict SKU grouping.
- Method: We compare Jaccard (Set) vs. TF-IDF (Vector) and propose a normalization pipeline.
- Result: Our method achieved X% accuracy with Y% reduction in computational time.
Set-Theoretic Approaches for Product Grouping (Jaccard)
- Concept: Treats text as a “Bag of Words” (BoW) without weights.
- Key Papers/Concepts:
- Cohen et al. (2003) often discuss string metrics for entity matching.
- Shingling / MinHash: In large datasets, calculating Jaccard for all pairs is $O(N^2)$. Literature focuses on Locality Sensitive Hashing (LSH) (MinHash) to approximate Jaccard similarity efficiently.
- Pros in Literature: High interpretability, excellent for “near-duplicate” detection.
- Cons: Fails when synonyms are used (e.g., “pants” vs “trousers”) or when word importance varies.
Vector Space Models (TF-IDF)
- Concept: Maps text to a high-dimensional Euclidean space.
- Key Papers/Concepts:
- Salton et al. (1975) (The foundational VSM paper).
- Character n-grams: Papers often cite that for noisy user-generated content (UGC), character n-grams outperform word tokens because they handle misspellings morphologically.
- Pros: Handles “rare words” (like model numbers) better due to IDF (Inverse Document Frequency) weighting.
The State-of-the-Art (Deep Learning / Embeddings)
- If you publish, reviewers will ask: “Why not BERT?”
- SBERT (Sentence-BERT): Current SOTA uses transformer models to generate dense vector embeddings.
- Your Counter-Argument: Deep learning is computationally expensive and “black-box”. For industrial product grouping where exact feature matching (like “Samsung” + “Galaxy”) is critical, classical methods (TF-IDF/Jaccard) often offer better precision and control than semantic embeddings which might group “iPhone 12” with “Samsung S20” because they are both “phones”.
II. Related Work
- Mention LSH (Locality Sensitive Hashing) for Jaccard.
- Mention DBSCAN and Agglomerative Clustering as standard algorithms.
- Cite limitations of BERT in high-precision SKU matching.
- Product Grouping for E-commerce
III. Methodology (The Core)
Group definition in dataset
- Define SKU vs Product Group (Parent-Child relationship).
- The challenge: “Noise” in titles (e.g.,
500ml,XL,Pack of 2).
- Preprocessing – The Normalization Filter for Product Grouping for E-commerce:
- Define your Regex rules mathematically.
- $T_{clean} = f(T_{raw})$ where $f$ removes tokens $t \in \{Dimensions, Colors, Stopwords\}$.
- Representation:
- Approach A (Jaccard): $J(A,B) = \frac{|A \cap B|}{|A \cup B|}$
- Approach B (TF-IDF): Cosine Similarity $\cos(\theta) = \frac{A \cdot B}{||A|| ||B||}$
- Clustering Algorithm:
- Explain why you chose Connected Components (Graph theory) for Jaccard or Hierarchical Clustering for TF-IDF.
IV. Experiments (Product Grouping for E-commerce)
- Dataset: Your dataset of “several thousand products”.
- Metrics: You must measure quality.
- Precision: Are elements in the cluster actually the same product?
- Recall: Did we find all sizes of that product?
- F1-Score: Harmonic mean of the two.
V. Results & Discussion
- Hypothesis: Jaccard works better for clean data; TF-IDF works better for noisy data (typos).
- Observation: Jaccard is faster but TF-IDF + N-grams is more robust.
Novel Algorithm:
- Stage 1 (Blocking): Use Jaccard on tokens to quickly find “Candidate Pairs” (fast, rough filter).
- Stage 2 (Refinement): Use TF-IDF with Character N-grams on the candidates to calculate a precise similarity score (handles typos).
- Stage 3 (Decision): Hard threshold (e.g., >0.85).
Relevant Search Terms & Papers
Entity Resolution (ER) is the problem of identifying which records in a database refer to the same real-world entity. An exhaustive ER process involves computing the similarities between pairs of records, which can be very expensive for large datasets. Various blocking techniques can be used to enhance the performance of ER by dividing the records into blocks in multiple ways and only comparing records within the same block.
However, most blocking techniques process blocks separately and do not exploit the results of other blocks. In this paper (https://dl.acm.org/doi/pdf/10.1145/1559845.1559870), authors propose an iterative blocking framework where the ER results of blocks are reflected to subsequently processed blocks. Blocks are now iteratively processed until no block contains any more matching records.
Compared to simple blocking, iterative blocking may achieve higher accuracy because reflecting the ER results of blocks to other blocks may generate additional record matches. Iterative blocking may also be more efficient because processing a block now saves the processing time for other blocks.
- “Short Text Clustering for E-commerce”
- “Product Entity Resolution with Noise”
- “Comparison of Jaccard and Cosine Similarity in Text Mining”
- “Blocking techniques for Entity Resolution” (This is crucial for scaling to thousands/millions of products). Product Grouping for E-commerce.
Specific types of papers to look for:
- Koporec et al.: Papers on combining Jaccard with other metrics. https://www.sciencedirect.com/science/article/pii/S1364815225002981
- Ganesan et al.: Research on “abstractive summarization” or short-text clustering in retail.
FAQ: Product Grouping in Noisy E-Commerce Datasets
1. What does “noisy data” mean in e-commerce?
Noisy data refers to inconsistencies, errors, or irrelevant information in product listings—such as misspellings, incomplete descriptions, or duplicate entries—that make grouping products challenging.
2. Why is product grouping important?
Grouping similar products improves search accuracy, recommendation quality, and overall user experience. It also helps businesses manage inventory and pricing strategies more effectively
3. What are common challenges in product grouping?
- Variations in product names and descriptions
- Missing or incorrect attributes
- Multiple languages or regional differences
- Inconsistent categorization by sellers
4. Which techniques are used to handle noisy datasets?
- Text normalization (removing special characters, standardizing case)
- Tokenization and similarity measures (e.g., cosine similarity, Jaccard index)
- Machine learning models for clustering and classification
- Attribute-based matching using structured data
5. Can AI improve product grouping accuracy?
Yes. AI models like BERT or domain-specific embeddings can capture semantic meaning in product descriptions, making grouping more accurate even with noisy data.
6. How do I start implementing product grouping?
Begin with data cleaning and normalization, then apply similarity-based clustering or train a supervised model if labeled data is available.
