Article Recommendation System with Machine Learning

A recommendation system is one of the applications of data science that is used by almost any application or website today. Many websites today use a recommendation system to recommend articles to their readers. For example, Medium.com and even the website you’re currently reading this article on is also using a recommendation system to recommend articles to its readers. So, if you want to learn how to create an article recommendation system with machine learning, this article is for you. In this article, I will walk you through how to build an Article Recommendation System with Machine Learning using Python.

Article Recommendation System

There are many ways to create recommendation systems. To create an articles recommendation system, we need to focus on content rather than user interest. For example, if a user reads an article based on clustering, all recommended articles should also be based on clustering. So to recommend articles based on the content:

we need to understand the content of the article
match the content with all the other articles
and recommend the most suitable articles for the article that the reader is already reading

For this task, we can use the concept of cosine similarity in machine learning. Cosine similarity is a method of building recommendation systems based on the content. It is used to find similarities between two different pieces of text documents. So we can use cosine similarity to build an article recommendation system. In the section below, I will take you through how to build an article recommendation system with machine learning using Python.

Article Recommendation System using Python

To create an article recommendation system, I collected data about some of the articles on this website itself. So let’s import the necessary Python libraries and the dataset we need to create an articles recommendation system:

import pandas as pd
import numpy as np
from sklearn.feature_extraction import text
from sklearn.metrics.pairwise import cosine_similarity
data = pd.read_csv("https://raw.githubusercontent.com/amankharwal/Website-data/master/articles.csv", encoding='latin1')
data.head()

This dataset is completely ready to use to create a recommender system, so let’s use the cosine similarity algorithm and write a Python function to recommend articles:

	articles = data["Article"].tolist()
	uni_tfidf = text.TfidfVectorizer(input=articles, stop_words="english")
	uni_matrix = uni_tfidf.fit_transform(articles)
	uni_sim = cosine_similarity(uni_matrix)
	def recommend_articles(x):
	return ", ".join(data["Title"].loc[x.argsort()[–5:–1]])
	data["Recommended Articles"] = [recommend_articles(x) for x in uni_sim]
	data.head()

view raw article recommendation.py hosted with ❤ by GitHub

As you can see from the output above, a new column has been added to the dataset that contains the titles of all the recommended articles. Now let’s see all the recommendations for an article:

print(data["Recommended Articles"][22])

BIRCH Clustering in Machine Learning, Clustering Algorithms in Machine Learning, DBSCAN Clustering in Machine Learning, K-Means Clustering in Machine Learning

Index 22 contains an article on “agglomerated clustering”, and all the recommended articles are also based on the concepts of clustering, so we can say that this recommender system can also give great results in real-time.

Summary

So this is how you can build an articles recommendation system. A lot of websites today uses a recommendation system to recommend articles to their readers. I hope you liked this article on how to build an articles recommendation system with machine learning using Python. Feel free to ask your valuable questions in the comments section below.