https://github.com/mahikshith/zee-recommender-system
Building a recommender system based on movies, users , review database
https://github.com/mahikshith/zee-recommender-system
Last synced: 3 months ago
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Building a recommender system based on movies, users , review database
- Host: GitHub
- URL: https://github.com/mahikshith/zee-recommender-system
- Owner: mahikshith
- Created: 2024-08-19T14:39:22.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-09-16T13:42:16.000Z (9 months ago)
- Last Synced: 2025-01-14T13:16:32.293Z (5 months ago)
- Language: Jupyter Notebook
- Size: 6.89 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Zee Recommender System
Building a recommender system based on movies, users , review database
#### **What is a Recommender System?**
A recommender engine, or a recommendation system is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user would give to an item.
#### **Types of Recommender Systems -**
Recommender systems usually make use of either or both *Collaborative Filtering* and *Content-based Filtering* techniques.
#### **Collaborative Filtering**
Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future, and that they will like similar kinds of items as they liked in the past. The system generates recommendations using only information about rating profiles for different users or items. By locating peer users/items with a rating history similar to the current user or item, they generate recommendations using this neighborhood.
#### **Content-based Filtering**
Content-based filtering methods are based on a description of the item and a profile of the user's preferences. These methods are best suited to situations where there is known data on an item (name, location, description, etc.), but not on the user. Content-based recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user's likes and dislikes based on an item's features.
#### Comparing the results :
If a user watches the movie 'Liar Liar' then the recomemendations that we could show :
correlation
Liar Liar
1.000000
Mrs. Doubtfire
0.499927
Dumb & Dumber
0.459601
Ace Ventura: Pet Detective
0.458654
Home Alone
0.455967
Wedding Singer, The
0.429222
Wayne's World
0.424552
Cable Guy, The
0.420942
Tommy Boy
0.413143
Austin Powers: International Man of Mystery
0.411105
###############################################################################
cosine_Similarity
title
Liar Liar
1.000000
Mrs. Doubtfire
0.557067
Ace Ventura: Pet Detective
0.516861
Dumb & Dumber
0.512585
Home Alone
0.511204
Wayne's World
0.499368
Wedding Singer, The
0.497076
Austin Powers: International Man of Mystery
0.489473
There's Something About Mary
0.483263
League of Their Own, A
0.482074
#################################################################
knn_disatnce
title
Liar Liar
0.000000
Mrs. Doubtfire
0.442933
Ace Ventura: Pet Detective
0.483139
Dumb & Dumber
0.487415
Home Alone
0.488796
Wayne's World
0.500632
Wedding Singer, The
0.502924
Austin Powers: International Man of Mystery
0.510527
There's Something About Mary
0.516737
League of Their Own, A
0.517926