Recommendation System for Products
1. Introduction
Objective: Build a recommendation system to suggest products to users based on
their preferences and the preferences of similar users.
Purpose: Improve user engagement and increase sales by providing personalized
product recommendations.
2. Project Workflow
1. Problem Definition:
- Recommend products to users using
collaborative filtering techniques.
- Identify user preferences based on
purchase or rating history.
2. Data Collection:
- Source: User-product interaction
data (e.g., purchase history, ratings).
- Fields: User ID, Product ID,
Rating/Interaction Score.
3. Data Preprocessing:
- Handle sparse matrices and normalize
data.
4. Model Building:
- Use collaborative filtering
(user-based or item-based).
5. Evaluation:
- Assess the recommendation quality
using metrics like precision, recall, and F1 score.
6. Deployment:
- Integrate the recommendation system
into a web or mobile application.
3. Technical Requirements
- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Recommendation Models: Surprise,
SciPy, Scikit-learn
- Visualization: Matplotlib, Seaborn
4. Implementation Steps
Step 1: Setup Environment
Install required libraries:
```
pip install pandas numpy matplotlib seaborn scikit-learn surprise
```
Step 2: Load and Explore Dataset
Load the dataset containing user-product interactions:
```
import pandas as pd
data = pd.read_csv("user_product_data.csv")
print(data.head())
```
Explore the distribution of interactions:
```
print(data.describe())
```
Step 3: Preprocess Data
Handle missing data and create a user-item matrix:
```
data.fillna(0, inplace=True)
user_item_matrix = data.pivot_table(index='UserID', columns='ProductID',
values='Rating', fill_value=0)
```
Normalize the matrix to reduce bias:
```
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
normalized_matrix = scaler.fit_transform(user_item_matrix)
```
Step 4: Build Recommendation Model
1. User-Based Collaborative Filtering:
```
from sklearn.metrics.pairwise import cosine_similarity
user_similarity = cosine_similarity(normalized_matrix)
```
Recommend products for a specific user:
```
user_index = 0 # Index of the target
user
recommendations = user_item_matrix.iloc[user_index] +
user_similarity[user_index].dot(user_item_matrix)
top_recommendations = recommendations.sort_values(ascending=False).head(5)
print(top_recommendations)
```
2. Using Surprise Library for Model Building:
```
from surprise import Dataset, Reader, KNNBasic
from surprise.model_selection import train_test_split
reader = Reader(rating_scale=(1, 5))
data = Dataset.load_from_df(data[['UserID', 'ProductID', 'Rating']], reader)
trainset, testset = train_test_split(data, test_size=0.25)
algo = KNNBasic()
algo.fit(trainset)
predictions = algo.test(testset)
```
Step 5: Evaluate Model
Evaluate the model using metrics like precision and recall:
```
from surprise import accuracy
accuracy.rmse(predictions)
accuracy.mae(predictions)
```
Step 6: Deployment
Integrate the recommendation system into a web or mobile app using Flask,
Django, or Streamlit.
Provide users with dynamic recommendations based on their profiles and recent
interactions.
5. Expected Outcomes
1. Personalized product recommendations for users.
2. Enhanced user experience and engagement.
3. Improved business revenue through targeted suggestions.
6. Additional Suggestions
- Explore hybrid recommendation systems that combine collaborative filtering
with content-based filtering.
- Incorporate implicit feedback such as clicks or views to refine
recommendations.
- Use deep learning techniques for advanced recommendation systems.