Implementing-Movies-Recommender-Systems

implement various recommender system models and compare their performance.

1. General
2. Program Structure
3. Installation
4. Footnote

General

The goal is to predict user rating based on recommender systems. The file of ratings our learning process was based on includes 100005 records with the following features:

• user – The user’s unique identifier
• item – The item’s unique identifier
• rating – The rating that was given to the item by the user, it is in the range [0.5,5]
• timestamp – The timestamp in which the rating was given.

Program Structure

The file learners includes the implantation of the different learners:

• baseline model - 𝑟̂𝑢𝑖 = 𝑅̂ + 𝑏𝑢 + 𝑏𝑖 where 𝑅̂ is the average of all the ratings in the user-item ratings matrix 𝑅, 𝑏𝑢 is the average rating deviation for user 𝑢 and 𝑏𝑖 is the average rating deviation for item 𝑖.
• Neighborhood Recommender - based on 3 nearest neighbors.
• Competition Recommender - based on variation of k means with a adiption for the required task.
• LS Recommender - Uses regression model to predict the ratings

Main file includes the execution and train test and cleaning of the ratings file from bad records.

Installation

1.Open the terminal

2.Clone the project by:

    $ git clone https://github.com/elaysason/Implementing-And-Comparing-Recommender-Systens.git

3.Run the main.py file by:

    $ python3 main.py

Footnote

The output is the RMSE of each recommender which is equals to: