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https://github.com/mahikshith/zee-recommender-system

Building a recommender system based on movies, users , review database
https://github.com/mahikshith/zee-recommender-system

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Building a recommender system based on movies, users , review database

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# 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