https://github.com/zeuscoderbe/recommender-system

Content-based Filtering, Neighborhood-based Collaborative Filtering
https://github.com/zeuscoderbe/recommender-system

collaborative-filtering content-based-recommendation hybrid-recommender-system knn-regression machine-learning

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Content-based Filtering, Neighborhood-based Collaborative Filtering

• Host: GitHub
• URL: https://github.com/zeuscoderbe/recommender-system
• Owner: ZeusCoderBE
• Created: 2024-03-28T13:31:16.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-05T03:45:10.000Z (12 months ago)
• Last Synced: 2025-02-05T08:51:28.556Z (4 months ago)
• Topics: collaborative-filtering, content-based-recommendation, hybrid-recommender-system, knn-regression, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 2.83 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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.

  

![image](https://github.com/ZeusCoderBE/Recommender-System/assets/117000361/2714e86a-b43a-4bc2-b9ba-fc125739931e)

- 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.

![image](https://github.com/ZeusCoderBE/Recommender-System/assets/117000361/ed58ed37-ef56-4d87-bd19-38f33bcfa057)

### 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.