A recommender system, 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. It is widely used to make recommendations based on a user's past behaviours and preferences. Recommender systems are often based on the kNN (k-nearest neighbor) algorithm, which makes recommendations based on shared similar features. It can be used to solve both classification and regression problems.
The objective of this project is to build a kNN-based recommender system in order to predict the top 5 movie based on a given movie, in this case "The Post". As there is no need for classification or regression, the nearestNeighbors model and neighrbors() method are used to find the 5 most closely related films. This project does not include model performance testing due to the small size of the dataset.
- Data Exploration
- Building a kNN-based Recommender System
- Getting Recommendations
Python. Python is an interpreted, high-level and general-purpose programming language.
Integrated Development Environment (IDE). Any IDE that can be used to view, edit, and run Python code, such as:
Install the following packages in Python prior to running the code.
import pandas as pd
import numpy as np
from sklearn.neighbors import NearestNeighborsDownload the Python File CA06_kNN_based_Recommender_Engine and open it in the IDE.
This project is licensed under the MIT License.
The project template and dataset provided by Arin Brahma at Loyola Marymount University.