User Based Collaborative Filtering Recommender System

What is User Based Collaborative Filtering Recommender System?

User-based collaborative filtering makes recommendations based on user-product interactions in the past.The assumption behind the algorithm is that similar users like similar products.

How does the User Based Collaborative Filtering Recommender System work?

1- Find similar users based on interactions with common items. 2- Identify the items rated high by similar users but have not been exposed to the active user of interest. 3- Calculate the weighted average score for each item. 4- Rank items based on the score and pick the top n items to recommend.

Prepare for Use

Go algorithm.py and find

ITEM_RATINGS_PATH = Path
ITEM_NAMES_PATH = Path

Write your files path

How should the files be?

The order in the ITEM_RATINGS should be like this user id | item id | rating | ...

The order in the ITEM_NAMES should be like this movie id | movie title | ...

How to Use

from algorithm import recommend

recommend(user_id, n_recomm, n_neighbors)

parameters:
- user_id: int, user to generate recommendations for
- n_neighbors: int: the number of neighbors to use to generate rating predictions
- n_recomm: int, number of movies to recommend

returns: - pd.DataFrame with [movie_id, rating, movie name]