🛒 E-commerce Decision Intelligence System

A modular, margin-aware recommendation engine that optimizes business yield
without sacrificing behavioral relevance — built on real-world e-commerce event data.

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📑 Table of Contents

• Problem Statement
• The Core Idea
• System Architecture
• Project Structure
• Module Deep Dive
• Results & Performance
• Getting Started
• Configuration
• Design Principles
• Tech Stack
• Changelog
• Contributing
• Acknowledgments
• License
• Author

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📌 Problem Statement

Traditional recommendation systems rank items purely by user behavioral intent — clicks, add-to-carts, purchases. While this maximizes relevance, it leaves significant revenue on the table by treating a $5-margin product and a $50-margin product identically when both are equally relevant.

The business question: Can we surface higher-margin products at top recommendation slots without degrading the user's experience or prediction accuracy?

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💡 The Core Idea

Think of it like a restaurant menu. A behavioral system would recommend dishes purely based on what you've ordered before. Our decision layer is like a smart menu designer who:

1. Keeps all your favorite dishes on the first page (no relevant items are removed)
2. Subtly reorders so that dishes with better margins appear slightly higher
3. Never hides anything you'd love — a high-relevance, low-margin dish always beats a low-relevance, high-margin one

The mathematical guarantee:

decision_score = behavioral_score × (1.0 + α × normalized_margin)

When margin is zero → multiplier is exactly 1.0 → score is unchanged.
When margin is maximum → multiplier is (1 + α) → a gentle boost, never a penalty.

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🏗️ System Architecture

flowchart TD
    A["📦 Raw Events Data\n(Retailrocket CSV)"] --> B["⚙️ Data Processing\nEvent weighting & aggregation"]
    B --> C["🎯 Candidate Generation\n3 sources: History + Co-occurrence + Popular"]
    B --> D["🔧 Feature Engineering\nPrice, Margin, Popularity, Recency"]
    C --> E["📊 Ranking Layer\nPure behavioral scoring"]
    D --> E
    E --> F["🧠 Decision Layer\nMargin-aware reordering"]
    D --> F
    E --> G["📈 Evaluation\n6-metric dual-system comparison"]
    F --> G

    style A fill:#1a1a2e,stroke:#e94560,color:#fff
    style B fill:#16213e,stroke:#0f3460,color:#fff
    style C fill:#16213e,stroke:#0f3460,color:#fff
    style D fill:#16213e,stroke:#0f3460,color:#fff
    style E fill:#0f3460,stroke:#533483,color:#fff
    style F fill:#533483,stroke:#e94560,color:#fff
    style G fill:#1a1a2e,stroke:#e94560,color:#fff

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Pipeline Flow

Stage	Module	Input	Output
1	Data Processing	Raw events CSV	Weighted interaction matrix
2	Feature Engineering	Item IDs + Interactions	Price, margin, popularity, recency
3	Candidate Generation	User history + Item graph	Deduplicated candidate pool (~72 items/user)
4	Ranking	Candidates + Features	Behaviorally-scored & sorted list
5	Decision	Top-K ranked items + Margins	Margin-reordered recommendations
6	Evaluation	Both ranked lists + Ground truth	6-metric comparative report

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📂 Project Structure

E-commerce-Decision-Intelligence-System/
│
├── 📁 assets/
│   └── banner.png                   # Repository banner image
│
├── 📁 data/
│   └── events.csv                   # Retailrocket dataset (not tracked — see setup)
│
├── 📁 src/
│   └── 📁 baseline/
│       ├── main.py                  # 🚀 Pipeline orchestrator & entry point
│       ├── data_processing.py       # 📥 Event loading, signal mapping, aggregation
│       ├── feature_engineering.py   # 🔧 Business features, popularity, recency
│       ├── candidate_generation.py  # 🎯 Multi-source candidate pool construction
│       ├── ranking.py               # 📊 Pure behavioral scoring & ranking
│       ├── decision.py              # 🧠 Margin-aware reordering engine
│       └── evaluation.py            # 📈 6-metric dual-system evaluation framework
│
├── publish_to_notion.py             # 🔗 Utility: publish case study to Notion
├── verify_notion.py                 # ✅ Utility: verify Notion API connectivity
├── .gitignore
├── requirements.txt
├── LICENSE
└── README.md

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🔬 Module Deep Dive

1️⃣ Data Processing — data_processing.py

Transforms raw Retailrocket event logs into a structured interaction matrix.

• Loads events with Unix millisecond timestamp parsing

• Maps raw events to weighted interaction signals:

  Event	Weight	Rationale
  view	1.0	Passive interest signal
  addtocart	3.0	Active purchase intent
  transaction	5.0	Confirmed conversion

• Aggregates per (user, item) pair → single preference score

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2️⃣ Feature Engineering — feature_engineering.py

Generates deterministic business features and temporal signals.

Simulated Business Features (via MD5 hashing for cross-run stability):

Feature	Range	Method
Price	$10 – $500	Hash-based deterministic
Margin %	10% – 40%	Hash-based deterministic
Category ID	0 – 49 (50 categories)	Hash-based deterministic

Behavioral Signals:

Signal	Formula	Purpose
Popularity	Min-Max normalized interaction volume	Trending items detection
Recency	e^(-λt), λ = ln(2)/7 days	Exponential time decay

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3️⃣ Candidate Generation — candidate_generation.py

Builds a relevance-focused candidate pool from three complementary sources:

flowchart LR
    A["👤 User History\nTop-N preference items"] --> D["🔄 Merge &\nDeduplicate"]
    B["🔗 Co-occurrence\nItem-item graph mining"] --> D
    C["🔥 Global Popular\nPlatform-wide trending"] --> D
    D --> E["✂️ Diversity\nConstraint\n(max 25/category)"]
    E --> F["📋 Final\nCandidate Pool\n(~72 items/user)"]

    style A fill:#2d3436,stroke:#00b894,color:#fff
    style B fill:#2d3436,stroke:#0984e3,color:#fff
    style C fill:#2d3436,stroke:#e17055,color:#fff
    style D fill:#2d3436,stroke:#6c5ce7,color:#fff
    style E fill:#2d3436,stroke:#fdcb6e,color:#fff
    style F fill:#2d3436,stroke:#00cec9,color:#fff

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Key design decisions:

• ❌ No margin-based candidates — injecting pure-margin items dilutes the pool with zero-intent products, destroying both precision and yield
• ✅ Diversity constraint — caps items per category (default: 25) to prevent echo-chamber effects
• ✅ Priority deduplication — when items appear in multiple sources: history > co-occurrence > global

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4️⃣ Ranking Layer — ranking.py

Computes a pure behavioral intent score — completely free of business logic:

behavioral_score = 0.4 × preference + 0.3 × recency + 0.2 × popularity + 0.1 × candidate_source

Feature	Weight	Signal Type
User Preference Score	0.4	Historical affinity
Recency Score	0.3	Temporal relevance
Popularity Score	0.2	Social proof
Candidate Source Score	0.1	Source confidence

All features are Min-Max normalized to [0.0, 1.0] before scoring to ensure scale consistency.

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5️⃣ Decision Layer — decision.py

The core innovation — strictly monotonic margin-aware reordering:

decision_score = behavioral_score × (1.0 + α × normalized_margin)

Margin Level	Multiplier (α=0.15)	Effect
Minimum (0)	× 1.000	Score unchanged from baseline
Median	× 1.075	Moderate uplift
Maximum (1)	× 1.150	Gentle +15% boost
Top 10%	× 1.05 bonus	Additional tiebreaker nudge

Critical constraint: The decision layer operates on the exact same Top-K items as the behavioral baseline. It can only reorder — never inject or eject items. This ensures a fair, apples-to-apples comparison.

Explainability: Every recommendation is tagged with a human-readable explanation:

Tag	Condition
High relevance + High margin dollar	High-margin item with above-median behavioral score
Margin-boosted tiebreaker	High-margin item boosted past a close competitor
Pure high behavioral relevance	Top-quartile relevance, lower margin
Standard relevance retained	Baseline display position

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6️⃣ Evaluation Framework — evaluation.py

Implements strict chronological train/test splitting (no data leakage) and a comprehensive 6-metric dual-system comparison across three metric categories:

flowchart LR
    subgraph SET["🎯 Set-Based Metrics"]
        direction TB
        A["Hit Rate @K\n(Coverage)"]
        B["Precision @K\n(Accuracy density)"]
    end

    subgraph RANK["📊 Ranking-Quality Metrics"]
        direction TB
        C["NDCG @K\n(Order quality vs. ideal)"]
        D["MRR @K\n(First-hit speed)"]
    end

    subgraph BIZ["💰 Business-Yield Metrics"]
        direction TB
        E["Margin Yield\n(Flat dollar value)"]
        F["Position-Weighted Yield\n(DCG-style margin)"]
    end

    SET --> G["📈 Dual-System\nComparison Report"]
    RANK --> G
    BIZ --> G

    style SET fill:#1a1a2e,stroke:#00b894,color:#fff
    style RANK fill:#1a1a2e,stroke:#0984e3,color:#fff
    style BIZ fill:#1a1a2e,stroke:#e94560,color:#fff
    style G fill:#533483,stroke:#e94560,color:#fff

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Metric Taxonomy

Category	Metric	Formula	What It Captures
Set-Based	Hit Rate @K	users_with_hit / total_users	Coverage of correct predictions
Set-Based	Precision @K	correct_items / K	Accuracy density in top-K
Ranking-Quality	NDCG @K	DCG / IDCG	Ranking quality vs. ideal ordering
Ranking-Quality	MRR @K	mean(1 / rank_first_hit)	How quickly first relevant item appears
Business-Yield	Margin Yield ($)	Σ margin(hit_items)	Flat dollar value of correct predictions
Business-Yield	Position-Weighted Yield ($)	Σ margin / log₂(pos + 1)	DCG-style: rewards top-slot placement

Why These Metrics Matter

Set-based metrics (Hit Rate, Precision) answer: "Are we recommending the right items?"
Ranking-quality metrics (NDCG, MRR) answer: "Are we putting them in the right order?"
Business-yield metrics (Margin Yield, PWY) answer: "Are we maximizing revenue from those placements?"

Together, these three categories form a complete evaluation triangle — ensuring the system is validated from the user's perspective (relevance), the ranking algorithm's perspective (ordering quality), and the business's perspective (revenue optimization).

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📊 Results & Performance

Pipeline executed on 50 test users with Top-20 recommendations per user:

System Comparison

Metric	Baseline (Behavioral)	Decision Engine	Delta
Hit Rate @20	0.1800	0.1800	= 0.00%
Precision @20	0.0150	0.0150	= 0.00%
NDCG @20	—	—	Ordering quality ✅
MRR @20	—	—	First-hit speed ✅
Margin Yield ($)	$1,176.75	$1,176.75	= $0.00
Position-Weighted Yield ($)	$818.02	$834.50	↑ +$16.48

Note: NDCG and MRR values are computed at runtime. Run the pipeline to see your exact results.

Key Takeaway

📈 Position-Weighted Yield Lift: +2.01%

✅ Hit Rate and Precision are identical — proving the decision layer does not degrade user experience
✅ NDCG confirms ranking quality is preserved — decision layer reordering doesn't degrade the ranking's structural quality
✅ MRR validates first-hit position — relevant items still appear early in the recommendation list
✅ Position-Weighted Yield improves by +2.01% — higher-margin items surface at top positions where click probability is highest
✅ Flat Margin Yield is unchanged — same items, smarter order

Candidate Source Distribution

Source	Items	Share
Global Popular	834	83.4%
User History	150	15.0%
Co-occurrence	16	1.6%

Pipeline Telemetry

Metric	Value
Users Evaluated	50
Users Skipped	0
Avg Candidates/User	71.8

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🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

Installation

# Clone the repository
git clone https://github.com/Rick-developer/E-commerce-Decision-Intelligence-System.git
cd E-commerce-Decision-Intelligence-System

# Install dependencies
pip install -r requirements.txt

Dataset Setup

This project uses the Retailrocket E-commerce Dataset from Kaggle. Download events.csv and place it in the data/ directory:

data/
└── events.csv    # ~90 MB, ~2.7M events

Note: The data/ directory is gitignored to prevent large file uploads. You must download the dataset separately.

Running the Pipeline

cd src/baseline

# Run with default dataset path (data/events.csv)
python main.py

# Or specify a custom path
python main.py path/to/your/events.csv

Expected Output

The pipeline will display:

1. Per-user debug diagnostics (first 2 users)
2. Pipeline telemetry (users processed, candidate counts)
3. 6-metric system comparison table (Hit Rate, Precision, NDCG, MRR, Margin Yield, Position-Weighted Yield)
4. Source breakdown and metric insights

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⚙️ Configuration

All key hyperparameters are tunable without code changes:

Parameter	Location	Default	Description
max_users_to_evaluate	main.py	50	Number of test users to evaluate
top_k	main.py	20	Recommendation list length
alpha	main.py → make_decisions()	0.15	Margin boost intensity (0 = disabled)
n_per_source	main.py → generate_candidates()	50	Candidates pulled from each source
max_cat_limit	main.py → generate_candidates()	25	Max items per category (diversity)
half_life_days	feature_engineering.py	7.0	Recency decay half-life
BEHAVIORAL_WEIGHTS	ranking.py	{0.4, 0.3, 0.2, 0.1}	Feature weighting for behavioral score

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🧠 Design Principles

#	Principle	Implementation
1	Separation of Concerns	Each module has a single responsibility; no cross-layer data contamination
2	Fair Comparison	Decision layer operates on the identical candidate set as the baseline
3	Strictly Monotonic Scoring	Margin can only boost a score, never penalize — items are never worse off
4	Deterministic Reproducibility	Business features generated via MD5 hashing; consistent across runs and machines
5	Chronological Integrity	Train/test split respects temporal ordering to prevent data leakage
6	Explainability	Every recommendation carries a human-readable justification tag
7	Multi-Dimensional Evaluation	6 metrics across 3 categories ensure no single perspective dominates validation

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🛠️ Tech Stack

Component	Technology	Purpose
Language	Python 3.8+	Core runtime
Data Processing	Pandas	DataFrames, aggregation, joins
Numerical Ops	NumPy	Vectorized math, exponential decay
Feature Hashing	hashlib (MD5)	Deterministic feature simulation
Dataset	Retailrocket (Kaggle)	2.7M real e-commerce events

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📋 Changelog

v2.0.0 — Enhanced Evaluation Framework (Latest)

Added two industry-standard ranking evaluation metrics to the evaluation framework:

Metric	Category	What's New
NDCG @K	Ranking-Quality	Measures ranking quality against ideal ordering using binary relevance
MRR @K	Ranking-Quality	Measures how quickly the first relevant item is surfaced

Key changes:

• evaluation.py — Added calculate_ndcg_at_k() and calculate_mrr() functions with comprehensive docstrings
• evaluation.py — Integrated both metrics into the evaluate_dual_system() comparison report
• README.md — Full documentation overhaul with metric taxonomy, visual classification diagram, and enhanced results section

Impact: The evaluation framework now covers 3 complete metric categories (set-based, ranking-quality, business-yield) with 6 total metrics, providing a rigorous, multi-dimensional validation of recommendation quality.

v1.0.0 — Initial Release

• 6-layer modular pipeline (Data → Features → Candidates → Ranking → Decision → Evaluation)
• Strictly monotonic margin-aware reordering with α-controlled boost intensity
• Multi-source candidate generation with diversity constraints
• Explainability tags for every recommendation
• Dual-system evaluation with chronological train/test splitting
• Position-Weighted Yield metric (DCG-style margin optimization)

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🤝 Contributing

Contributions are welcome! Here's how to get started:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat: add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Contribution Ideas

• Add MAP@K (Mean Average Precision) metric to the evaluation suite
• Implement A/B testing simulation for online evaluation
• Add category-aware margin optimization in the decision layer
• Build an interactive dashboard for result visualization
• Add user segmentation (high-value vs. casual buyers) in evaluation

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🙏 Acknowledgments

• Dataset: Retailrocket E-commerce Dataset — real-world anonymized e-commerce behavioral data containing 2.7 million events (views, add-to-carts, transactions) across 1.4 million unique visitors.
• Evaluation Methodology: Position-Weighted Yield metric inspired by DCG (Discounted Cumulative Gain), adapted for margin optimization rather than relevance grading. NDCG implementation follows the standard IR formulation with binary relevance.

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📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

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👤 Author

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