🛒 E-commerce Recommendation System with TensorFlow

A recommendation system is crucial for enhancing user experience and driving sales in e-commerce platforms. This tutorial demonstrates how to build a collaborative filtering model using TensorFlow to predict user preferences for products.

📊 Dataset Overview

• Data Source: MovieLens (adapted for e-commerce)
• Features:
  • User IDs
  • Product IDs
  • Historical purchase data
  • Rating scores (1-5)
• Sample Input:

  user_id,product_id,rating
  101,202,4.5
  102,205,3.8
  

🧠 Model Architecture

1. Embedding Layer
   Convert categorical data into dense vectors:

   user_embedding = tf.keras.layers.Embedding(input_dim=users, output_dim=8)(user_ids)
   product_embedding = tf.keras.layers.Embedding(input_dim=products, output_dim=8)(product_ids)
   

2. Concatenation & Dense Layers
   Merge embeddings and apply fully connected layers:

   merged = tf.keras.layers.Concatenate()([user_embedding, product_embedding])
   dense = tf.keras.layers.Dense(16, activation='relu')(merged)
   output = tf.keras.layers.Dense(1)(dense)
   

3. Training Objective
   Use Mean Absolute Error (MAE) to minimize prediction gaps.

🚀 Implementation Steps

• Preprocess data with tf.data.Dataset
• Split into training/validation sets
• Compile model with Adam optimizer
• Train for 10 epochs with batch size 32

📚 Extend Your Knowledge

Explore TensorFlow's Graph Neural Networks for more complex recommendation scenarios.