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A Complete End-to-End Machine Learning Based Recommendation Project
Latest   Machine Learning

A Complete End-to-End Machine Learning Based Recommendation Project

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August 17, 2022
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Last Updated on July 26, 2023 by Editorial Team

Author(s): Gowtham S R

Originally published on Towards AI.

A machine learning recommendation project based on collaborative filtering and popularity-based filtering

image from Unsplash uploaded by Toa Heftiba

What is a recommendation system?

Whenever we visit a shopping mall to buy a new pair of shoes or clothes, we find a dedicated person who helps us with the kind of products we should buy based on our preferences and makes our job simpler. In simple words, he is a recommendation system. But in this modern world, everything is online, and there is so much content on the internet, there are crores of videos on Youtube and crores of products on Amazon, which makes it difficult for the user to choose. There comes the recommendation system, which makes the user's life simple by recommending the next video to watch or a similar product to buy.

A recommendation system is a piece of code that is intelligent enough to understand the user’s preferences and recommend things based on his/her interest, the goal is to increase profitability. For Eg, Youtube and NetFlix want you to spend more time on their platform, so they recommend videos based on the user’s preferences. Amazon wants you to buy products from their website so that they can make more profit.

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Types of Recommendation Systems:

Image by author

Popularity-based: Recommending the top products from their website to every user. This method will not consider the user’s interest. Eg, the Trending section on Youtube, IMDB top 250 movies.

Content-based:

Image by author

This is based on the similarity between the products. E.g., If a user has watched a movie and liked it, he may like to watch similar movies in the future. This can be based on the genre, actor, actress, or director.

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Collaborative filtering:

Image by author

This is based on the similarity of users. E.g., If person A and B had watched and liked the movie M, next if person A watched the movie Z and liked it, we can recommend the movie Z to person B since A and B are similar users.

Hybrid filtering: This makes use of all or some of the above-mentioned methods to form a hybrid model.

Building a Book Recommendation System:

Let us look at how we can build a popularity-based and collaborative filtering-based book recommendation system.

Image by author

Popularity-based filtering :

Let us import basic libraries, read the dataset and create the data frames. The dataset can be downloaded from this link — dataset.

books — the ‘books’ data frame has 2 columns ISBN(unique ID for each book) and Book-Title.

users — the ‘users’ data frame has 3 columns User-ID, Location, and Age.

ratings — the ‘ratings’ data frame has 3 columns User-ID, ISBN, and Book-Rating.

Let us look at the first 5 rows from each data frame.

Image by author

The next step is to merge books and ratings on the column ‘ISBN’ and merge users and ratings on the column ‘USER-ID’.

Image by author

Let us make 2 new data frames that will have a number of ratings and the average rating for each book and name them book_num_ratings and book_avg_ratings.

final_rating data frame is created by merging the two data frames book_avg_ratings and book_num_ratings

Image by author

In order to implement popularity-based filtering, let us select only those books with more than 250 ratings sorted in descending order by avg_ratings,

Now we have successfully built a popularity-based recommendation system where we have filtered out the top 50 books from our dataset.

Image by author

Collaborative Filtering:

To build a collaborative-filtering-based recommendation system, we will consider only those users who have rated more than 200 books and those books which are having at least 50 ratings.

We have a final data frame that has only those books with at least 50 ratings and users who have rated at least 200 books.

Image by author

The next step is to create a pivot table that will have ‘Book-Title’ as the index, ‘User-ID’ as the column, and ‘Book-Rating’ as the value.

Image by author

We will calculate the similarity score between each book using the cosine_similarity function. 5 books with the highest similarity scores will be recommended as shown below.

Below are a few results based on our recommendation model.

Image by author
Image by author

Now we have successfully built a popularity-based and collaborative filtering-based recommendation system. Let us deploy these models using flask.

Deploying the model:

Let us use the flask framework to deploy the model. We need to create a python file app.py, which will be linked to HTML files.

We should create a folder called templates where the HTML files index.html and recommend.html will be placed.

We need to place pickle files popularity.pkl, similarity.pkl, books.pkl, pt.pkl in the project folder.

Complete code can be found on the GitHub page

Popularity-based filtering: The home page of our app will show the top 50 books from our dataset along with the cover page, author, average rating, and the total number of votes.

Image by author

Collaborative filtering: When a user clicks the recommend icon, he will be prompted to the below page where a user can select the book for which he needs a recommendation, once submitted, similar books along with the cover page and author will be shown.

Image by author
Image by author

So, we have successfully completed the deployment part. You can try to implement this project this weekend.

Thank you.

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//Articles to skip let articleIdsToSkip = ['post-2651', 'post-3414', 'post-3540']; //keyword with its related achortag is recieved here along with article id function searchAndReplace(keyword, anchorTag, articleId) { //selects the h3 h4 and p tags that are inside of the article let content = document.querySelector(`#${articleId} .entry-content`); //replaces the "linktext" in achor tag with the keyword that will be searched and replaced let newLink = anchorTag.replace('linktext', keyword); //regular expression to search keyword var re = new RegExp('(' + keyword + ')', 'g'); //this replaces the keywords in h3 h4 and p tags content with achor tag content.innerHTML = content.innerHTML.replace(re, newLink); } function articleFilter(keyword, anchorTag) { //gets all the articles var articles = document.querySelectorAll('article'); //if its zero or less then there are no articles if (articles.length > 0) { for (let x = 0; x < articles.length; x++) { //articles to skip is an array in which there are ids of articles which should not get effected //if the current article's id is also in that array then do not call search and replace with its data if (!articleIdsToSkip.includes(articles[x].id)) { //search and replace is called on articles which should get effected searchAndReplace(keyword, anchorTag, articles[x].id, key); 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