TauraiUCB / Recommendation-Engine

Build a recommendation engine that seeks to filter user preferences according to the user's choices and browsing history.

Recommendation Engine

Type of Data:

• There are 17770 unique movie IDs.
• There are 480189 unique user IDs.
• There are ratings. Ratings are on a five star (integral) scale from 1 to 5.
• There is a date on which the movie is watched by the user in the format YYYY-MM-DD.

Content-based Filtering:

Content-based Filtering works on the principle that people who agreed in the past will agree in the future, means based on user’s previous preferences it recommends the product.

Collaborative Filtering:

Collaborative Filtering works on a principle of correlation. It considers the common interest shared by two or more people.

AutoEncoders:

Autoencoder is an unsupervised artificial neural network that learns how to efficiently compress and encode data then learns how to reconstruct the data back from the reduced encoded representation to a representation that is as close to the original input as possible.

Boltzmann Machine:

A restricted Boltzmann machine is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.

For data and explanation of code, visit to my blogging website - https://capablemachine.com/

Blog:-

Build your own Movie Recommendation Engine using Word Embedding

• https://capablemachine.com/2020/06/23/build-your-own-recommendation-engine-using-word2vec-model/
• https://medium.com/@sarangdeshmukh783/word-embedding-movie-recommendation-system-2ffdce9d323f