/RecommenderSystem

Primary LanguageJupyter Notebook

RecommenderSystem

This is a CZ4032 Project 1 implementing Recommender System onto datasets for data analysis.

Members

Tan Jiawei, Eric
Zon Liew Hur Zhen
Chan Jun Jie
Seph Chen Kian Leong
Samuel Png Yao Wei

Requirements

  1. Working Editor / Anaconda with Python installed
  2. pip install packages inside requirements.txt (Surprise, wordcloud, pandas, scipy, numpy, re, time, nltk)

Methods

Collaborative Filtering
Comment-Based Filtering

How to use the recommender systems?

Collaborative Filtering

  1. KNN Collaborative - Optimization will take approximately an hour hence comment it out as it is not required.
    get_rec_knn([("movie_title", rating)
  2. Search Similarity - new_user1 = [("movie_title", rating), ("move_title", rating)] -> adjust the user's watch list and rating to retrieve recommendations.
  3. Similar Genre Average Ratings - get_recommendation_netflix(["genre" , "genre"],[]) -> adjust the user's favourite genre for recommendations

Content Filtering

  1. Netflix Dataset Recommendation - recommend_netflix(['Movie_Title']) -> Returns you the top 5 netflix recommendation
  2. IMDB Movie Dataset Recommendation - movie_recommendation(['Movie_Title']) -> Returns you the top 5 movie recommendation

Data Sets

For Content Based Filtering:
IMDB Movies Top 1000 : https://www.kaggle.com/datasets/harshitshankhdhar/imdb-dataset-of-top-1000-movies-and-tv-shows
Netflix: https://www.kaggle.com/datasets/shivamb/netflix-shows

For Collaborative Based Filter:
netflix_titles.csv
userDatas.csv (own data in github)
movies.csv
ratings.csv : https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa21xZjQyNGE0Qkx0NkFVOUZYM3h6OE4zampBZ3xBQ3Jtc0tsRGM5RHFtWFdXUFdIRTYyQnd0cUJxeldVQWtlaXBYMVQ1cFBRSWhLRk9JUjdsWnBadHJNdWl3QXV3Zkx3QUJwZ1k5ZmFsdTZpODJ5UXEwUjlrdkJFcU5HMmxhQ1ZXY0p6ZHFRQjRlRDJiM2Y1OGE1SQ&q=https%3A%2F%2Fdrive.google.com%2Ffile%2Fd%2F1WWQCl9w52M1sXNWd4JSKL7q-HHywk03p%2Fview%3Fusp%3Dsharing&v=3ecNC-So0r4