Movie Recommender for MovieLens 100K Dataset using Singular Value Decomposition
svd.py - Simple Movie recommender using SVD (sample output-svd.png)
2)
u.data --The full u data set, 100000 ratings by 943 users on 1682 items. Each user has rated at least 20 movies. Users and items are numbered consecutively from 1. The data is randomly ordered. This is a tab separated list of
user id | item id | rating | timestamp.
The time stamps are unix seconds since 1/1/1970 UTC
u.item -- Information about the items (movies); this is a tab separated list of
movie id | movie title | release date | video release date | IMDb URL | unknown | Action | Adventure | Animation | Children's | Comedy | Crime | Documentary | Drama | Fantasy | Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi | Thriller | War | Western |
The last 19 fields are the genres, a 1 indicates the movie is of that genre, a 0 indicates it is not; movies can be in several genres at once. The movie ids are the ones used in the u.data data set.
i)svdtest.py -- To ensure proper working of Recommender system for 100K Dataset with 600 principal components preserving 92% variance of the data (sample output-svdtest1.png)
ii)svd_movielens.py -- Movie Recommender system for 100K Dataset with 600 principal components preserving 92% variance of the data. (sample output-svd_movielens1.png)
iii)svd_dr.py -- Movie Recommender to choose Principal components based on the percentage of variance of data to be preserved.(sample output-svd_dr1.png)