Restaurant Recommendation System

Overview

This project implements a collaborative filtering recommendation system for a restaurant's meal ordering application. The recommendation system aims to enhance user experience by providing personalized meal suggestions based on the preferences and ordering history of users.

Collaborative Filtering

Collaborative filtering is a popular technique in recommendation systems that leverages the preferences and behaviors of users to generate recommendations. The key idea is to identify users with similar preferences and recommend items that they have liked but the target user has not yet interacted with.

Techniques Used

1. User-Item Matrix Creation:

   • The foundation of collaborative filtering is the construction of a user-item interaction matrix, where rows represent users, columns represent items (meals), and the entries indicate user interactions (e.g., orders, clicks).

2. Data Pre-processing:

   • Data collected from the restaurant's application undergoes pre-processing to handle missing values, remove duplicates, and normalize timestamps. The cleaned data is then used to create the user-item matrix.

3. Normalization and Scaling:

   • The user-item matrix undergoes normalization and scaling to ensure that all interactions contribute proportionally to the collaborative filtering model. Techniques such as log transformation and min-max scaling are applied.

4. Collaborative Filtering Model:

   • The collaborative filtering model calculates similarities between users or items based on their interactions. The model identifies users with similar preferences and recommends items that these users have liked but the target user has not.