Instagram is one of the popular social media applications today. People use Instagram to share photos and videos. One of the reasons behind the popularity of Instagram is its recommendation system. It helps in grabbing the attention of the user for a longer time. So, if you want to learn how to use machine learning to recommend Instagram posts, this article is for you. In this article, I will take you through how to create an Instagram Recommendation System with Machine Learning using Python.
How does Instagram Recommendation System Works?
The post you see as a suggested post on Instagram when you scroll through your feed is where Instagram uses a recommendation system to recommend posts that may interest you.
Instagram also use a recommender system to suggest more accounts to follow, but here I will take you through how the recommender system of Instagram recommends more posts.
The suggested posts you see on Instagram are recommended based on your activities on Instagram, such as:
- What kind of accounts do you follow, and what kind of posts do you engage with
- The caption of the posts that you engage with also plays a role in suggesting more similar posts
- How do other users with similar interests as yours engage with the posts
So these were the most common factors for suggesting more posts on Instagram. In the section below, I will take you through how to create an Instagram Recommendation system by using the captions of the Instagram posts.
Instagram Recommendation System using Python
The dataset I am using for creating an Instagram recommendation system is collected from my Instagram account. You can download the dataset from here. Now let’s import the necessary Python libraries and the dataset to get started with this task:
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("Instagram data.csv")
print(data.head()) Date Impressions From Home From Hashtags From Explore \
0 2021-12-10 3920 2586 1028 619
1 2021-12-11 5394 2727 1838 1174
2 2021-12-12 4021 2085 1188 0
3 2021-12-13 4528 2700 621 932
4 2021-12-14 2518 1704 255 279
From Other Saves Comments Shares Likes Profile Visits Follows \
0 56 98 9 5 162 35 2
1 78 194 7 14 224 48 10
2 533 41 11 1 131 62 12
3 73 172 10 7 213 23 8
4 37 96 5 4 123 8 0
Conversion Rate Caption \
0 5.714286 Here are some of the most important data visua...
1 20.833333 Here are some of the best data science project...
2 19.354839 Learn how to train a machine learning model an...
3 34.782609 Here’s how you can write a Python program to d...
4 0.000000 Plotting annotations while visualizing your da...
Hashtags
0 #finance #money #business #investing #investme...
1 #healthcare #health #covid #data #datascience ...
2 #data #datascience #dataanalysis #dataanalytic...
3 #python #pythonprogramming #pythonprojects #py...
4 #datavisualization #datascience #data #dataana...
I will only choose the caption and the hashtags column for the rest of the task:
data = data[["Caption", "Hashtags"]] print(data.head())
Caption \
0 Here are some of the most important data visua...
1 Here are some of the best data science project...
2 Learn how to train a machine learning model an...
3 Here’s how you can write a Python program to d...
4 Plotting annotations while visualizing your da...
Hashtags
0 #finance #money #business #investing #investme...
1 #healthcare #health #covid #data #datascience ...
2 #data #datascience #dataanalysis #dataanalytic...
3 #python #pythonprogramming #pythonprojects #py...
4 #datavisualization #datascience #data #dataana...
Here I will use cosine similarity in machine learning to find similarities between the captions. After finding similarities between the posts, we can recommend them to the user based on the similarities to the post the user just interacted with. Below is how we can recommend Instagram posts:
captions = data["Caption"].tolist() uni_tfidf = text.TfidfVectorizer(input=captions, stop_words="english") uni_matrix = uni_tfidf.fit_transform(captions) uni_sim = cosine_similarity(uni_matrix) def recommend_post(x): return ", ".join(data["Caption"].loc[x.argsort()[-5:-1]]) data["Recommended Post"] = [recommend_post(x) for x in uni_sim] print(data.head())
Caption \
0 Here are some of the most important data visua...
1 Here are some of the best data science project...
2 Learn how to train a machine learning model an...
3 Here’s how you can write a Python program to d...
4 Plotting annotations while visualizing your da...
Hashtags \
0 #finance #money #business #investing #investme...
1 #healthcare #health #covid #data #datascience ...
2 #data #datascience #dataanalysis #dataanalytic...
3 #python #pythonprogramming #pythonprojects #py...
4 #datavisualization #datascience #data #dataana...
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Now let’s have a look at the recommendations of an Instagram post from the dataset:
print(data["Recommended Post"][3])
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So this is how you can recommend Instagram posts based on the captions of the posts.
Summary
The post you see as a suggested post on Instagram when you scroll through your feed is where Instagram uses a recommender system to recommend posts that may interest you. It helps in grabbing the attention of the user for a longer time. I hope you liked this article on creating an Instagram Recommendation System with Machine Learning using Python. Feel free to ask valuable questions in the comments section below.
if I use my downloaded data from instagram would this still function ?
yes