README.md

Recommendation System

Introduction

Environment Setup

1. Install Python libraries: numpy, scikit-learn,pandas.
2. Use data from the https://www.kaggle.com/datasets/prajitdatta/movielens-100k-dataset.

Data

The data used includes user information (age, gender, occupation),movies(Title,Genre) and their ratings for movies. This data is split into training(ua.base) and test(ua.test) sets.

The main idea of the two algorithms Content Filtering and Collaborative Filtering

• Content Filtering :Suggest items based on the user's profile or based on the content/attributes of items similar to items the user has selected in the past.

• Collaborative Filtering: Suggest items based on similarity between users and/or items. It can be understood that this is a way to suggest a user based on users with similar behavior.

I implemented two recommendation algorithms: Content Filtering and Collaborative Filtering.

1. Content Filtering:

   • I created a vector representation for each movie using TF- IDF (item profiles).

   • I trained a ridge regression model for each user to learn the weights(user profiles).

   • I used item profiles and user profiles to predict and recommend movie ratings.

2. Collaborative Filtering:

   • I utilized two approaches: item-item and user-user.

   • I calculated cosine similarity between items or users.

   • I implemented a KNN model by selecting K similar users/items to predict rating scores.

3. Hybrid between collaborative filtering and content filtering

• After predicting the rating in the test set, I combined the predicted rating in the two algorithm

• I reevaluated using the RMSE measure

Libraries and Technologies

• Programming Language: Python
• Main Libraries: NumPy, scikit-learn,pandas
• Model: Ridge Regression, TF-IDF Transformer,KNN User-User,KNN Item-Item

Performance Evaluation

• Utilize Root Mean Squared Error (RMSE) to assess the accuracy of the model on the test set.