{"id":117255,"date":"2026-06-19T22:59:27","date_gmt":"2026-06-19T17:29:27","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=117255"},"modified":"2026-06-19T22:59:29","modified_gmt":"2026-06-19T17:29:29","slug":"building-recommendation-systems-in-python","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/building-recommendation-systems-in-python\/","title":{"rendered":"Building a Recommendation System in Python from Scratch"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\"><strong>Quick TL;DR<\/strong><\/h2>\n\n\n\n<ul>\n<li>A recommendation system predicts what a user is likely to want next \u2014 based on their behavior, preferences, or similarity to other users. In Python, recommendation systems are built using collaborative filtering, content-based filtering, or hybrid approaches.&nbsp;<\/li>\n\n\n\n<li>This guide covers the core concepts, a step-by-step implementation using pandas and scikit-learn, and practical tips for taking a basic recommender from prototype to production-ready.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Introduction<\/strong><\/h2>\n\n\n\n<p>Every time Netflix suggests a show, Amazon surfaces a product, or Spotify generates a playlist \u2014 a recommendation system is working underneath. These systems are no longer exclusive to tech giants. With Python&#8217;s data science ecosystem, building a functional recommendation engine from scratch is achievable even without a machine learning background.<\/p>\n\n\n\n<p>This guide starts from zero \u2014 no prior ML experience required \u2014 and walks through the logic, the math, and the code behind a working recommendation system.<\/p>\n\n\n\n<p>Ready to build real machine learning projects like this and more? Explore <strong>HCL GUVI&#8217;s <\/strong><a href=\"https:\/\/www.guvi.in\/zen-class\/python-course\/?utm_source=blog&amp;utm_medium=hyperlink&amp;utm_campaign=building-a-recommendation-system-in-python-from-scratch\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Python Course<\/strong><\/a> \u2014 structured from core fundamentals through data science and ML applications, with hands-on projects, mentorship, and placement support built in.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Is a Recommendation System?<\/strong><\/h2>\n\n\n\n<p>A<a href=\"https:\/\/www.guvi.in\/blog\/what-is-a-recommendation-engine\/\" target=\"_blank\" rel=\"noreferrer noopener\"> recommendation system <\/a>is an <a href=\"https:\/\/www.guvi.in\/blog\/what-is-an-algorithm\/\" target=\"_blank\" rel=\"noreferrer noopener\">algorithm<\/a> that filters information to predict what a specific user would prefer from a larger set of options. It answers one question \u2014 given what I know about this user, what should I show them next?<\/p>\n\n\n\n<p>There are three primary approaches:<\/p>\n\n\n\n<ul>\n<li><strong>Collaborative Filtering<\/strong> \u2014 recommends based on what similar users liked. If User A and User B both liked the same five movies, and User B liked a sixth, that sixth gets recommended to User A.<\/li>\n\n\n\n<li><strong>Content-Based Filtering<\/strong> \u2014 recommends based on item attributes. If a user liked an action movie with a specific director, the system surfaces other action movies by the same director.<\/li>\n\n\n\n<li><strong>Hybrid Systems<\/strong> \u2014 combine both approaches to compensate for each method&#8217;s weaknesses.<\/li>\n<\/ul>\n\n\n\n<p>For beginners, collaborative filtering is the clearest starting point \u2014 it requires no item metadata, only user interaction data.<\/p>\n\n\n\n<p><strong>Read More: <\/strong><a href=\"https:\/\/www.guvi.in\/blog\/python-projects-for-beginners\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Top 10 Python Projects for Beginners with Source Code<\/strong><\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Setting Up the Environment<\/strong><\/h2>\n\n\n\n<p>Install the required libraries before writing any code:<\/p>\n\n\n\n<p>pip install pandas numpy scikit-learn<\/p>\n\n\n\n<p>The full implementation in this guide uses only these three <a href=\"https:\/\/www.guvi.in\/blog\/python-libraries-explained\/\" target=\"_blank\" rel=\"noreferrer noopener\">libraries <\/a>\u2014 no specialized <a href=\"https:\/\/www.guvi.in\/blog\/top-machine-learning-frameworks\/\" target=\"_blank\" rel=\"noreferrer noopener\">ML frameworks<\/a> required.<\/p>\n\n\n\n<div style=\"background-color: #099f4e; border: 3px solid #110053; border-radius: 12px; padding: 18px 22px; color: #FFFFFF; font-family: Montserrat, Helvetica, sans-serif; line-height: 1.6; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); max-width: 800px;\">\n  <strong style=\"font-size: 22px; color: #FFFFFF;\">\ud83d\udca1 Did You Know?<\/strong>\n  <p style=\"margin-top: 14px;\">\n    <strong>Netflix<\/strong> has stated that a large majority of viewing activity on its platform comes from its recommendation system rather than direct search. Its recommendation engine processes billions of user interactions to personalize content rankings, thumbnails, and suggestions in real time. While the system has evolved into a highly sophisticated machine learning pipeline, many of its foundational ideas are rooted in classic <strong>collaborative filtering<\/strong> techniques, where user behavior patterns and similarities between users or items are used to predict what someone is likely to watch next. This blend of large-scale data engineering and foundational recommender system concepts is what enables Netflix to keep users engaged at global scale.\n  <\/p>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>The Dataset<\/strong><\/h2>\n\n\n\n<p>For this guide, a simple user-movie ratings matrix is used \u2014 the same structure behind most real-world recommendation engines:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>import pandas <strong>as<\/strong> pd<br>import numpy <strong>as<\/strong> np<br><br>data = {<br>&nbsp; &nbsp; &#8216;user&#8217;: [&#8216;Alice&#8217;, &#8216;Alice&#8217;, &#8216;Alice&#8217;, &#8216;Bob&#8217;, &#8216;Bob&#8217;, &#8216;Carol&#8217;, &#8216;Carol&#8217;, &#8216;Carol&#8217;, &#8216;Dave&#8217;, &#8216;Dave&#8217;],<br>&nbsp; &nbsp; &#8216;movie&#8217;: [&#8216;Inception&#8217;, &#8216;Interstellar&#8217;, &#8216;The Dark Knight&#8217;, &#8216;Inception&#8217;, &#8216;The Dark Knight&#8217;,<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &#8216;Interstellar&#8217;, &#8216;The Dark Knight&#8217;, &#8216;Dunkirk&#8217;, &#8216;Inception&#8217;, &#8216;Dunkirk&#8217;],<br>&nbsp; &nbsp; &#8216;rating&#8217;: [5, 4, 5, 4, 5, 3, 4, 5, 4, 3]<br>}<br><br>df = pd.DataFrame(data)<br><strong>print<\/strong>(df.head())<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>This creates a long-format dataframe \u2014 each row is one user rating one movie. The next step is converting this into a matrix format the algorithm can process.<\/p>\n\n\n\n<p>Ready to build real machine learning projects like this and more? Explore <strong>HCL GUVI&#8217;s <\/strong><a href=\"https:\/\/www.guvi.in\/zen-class\/python-course\/?utm_source=blog&amp;utm_medium=hyperlink&amp;utm_campaign=building-a-recommendation-system-in-python-from-scratch\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Python Course<\/strong><\/a> \u2014 structured from core fundamentals through data science and ML applications, with hands-on projects, mentorship, and placement support built in.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Building the User-Item Matrix<\/strong><\/h2>\n\n\n\n<p>The user-item matrix places users on one axis and items on the other \u2014 each cell holds the rating that user gave that item. Empty cells represent movies a user has not rated yet \u2014 these are the gaps the recommendation system fills.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>user_item_matrix = df.pivot_table(index=&#8217;user&#8217;, columns=&#8217;movie&#8217;, values=&#8217;rating&#8217;)<br>user_item_matrix = user_item_matrix.fillna(0)<br><strong>print<\/strong>(user_item_matrix)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>movie &nbsp; &nbsp; &nbsp; Dunkirk&nbsp; Inception&nbsp; Interstellar&nbsp; The Dark Knight<br>user<br>Alice &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 0.0&nbsp; &nbsp; &nbsp; &nbsp; 5.0 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 4.0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 5.0<br>Bob &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 0.0&nbsp; &nbsp; &nbsp; &nbsp; 4.0 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 0.0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 5.0<br>Carol &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 5.0&nbsp; &nbsp; &nbsp; &nbsp; 0.0 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 3.0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 4.0<br>Dave&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 3.0&nbsp; &nbsp; &nbsp; &nbsp; 4.0 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 0.0&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; 0.0<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Zeros represent unrated movies \u2014 not actual zero ratings. This distinction matters when interpreting model output.<\/p>\n\n\n\n<div style=\"background-color: #099f4e; border: 3px solid #110053; border-radius: 12px; padding: 18px 22px; color: #FFFFFF; font-family: Montserrat, Helvetica, sans-serif; line-height: 1.6; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); max-width: 800px;\">\n  <strong style=\"font-size: 22px; color: #FFFFFF;\">\ud83d\udca1 Did You Know?<\/strong>\n  <p style=\"margin-top: 14px;\">\n    According to widely cited industry research from firms like <strong>McKinsey<\/strong>, recommendation systems play a major role in driving user engagement and revenue across large-scale digital platforms. For example, personalization and recommendation algorithms are estimated to contribute significantly to product discovery on platforms like <strong>Amazon<\/strong> and content consumption on <strong>Netflix<\/strong>. Beyond their business impact, recommendation systems have become one of the most in-demand areas in machine learning and data science. In 2026, skills such as collaborative filtering, ranking models, and embedding-based retrieval continue to appear frequently in Python-focused ML job descriptions globally, including in India, reflecting the importance of personalization in modern digital products.\n  <\/p>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Generating Recommendations<\/strong><\/h2>\n\n\n\n<p>With similarity scores in place, recommendations are generated by \u2014 finding the most similar users to the target user, identifying movies those users rated highly that the target user has not seen, and weighting those ratings by similarity score:]<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td>def get_recommendations(target_user, user_item_matrix, similarity_df, n=3):<br>&nbsp; &nbsp; similar_users = similarity_df[target_user].drop(target_user).sort_values(ascending=<strong>False<\/strong>)<br>&nbsp; &nbsp; target_ratings = user_item_matrix.loc[target_user]<br>&nbsp; &nbsp; unseen_movies = target_ratings[target_ratings == 0].index<br><br>&nbsp; &nbsp; scores = {}<br>&nbsp; &nbsp; <strong>for<\/strong> movie in unseen_movies:<br>&nbsp; &nbsp; &nbsp; &nbsp; weighted_sum = 0<br>&nbsp; &nbsp; &nbsp; &nbsp; similarity_sum = 0<br>&nbsp; &nbsp; &nbsp; &nbsp; <strong>for<\/strong> user, similarity in similar_users.items():<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; rating = user_item_matrix.loc[user, movie]<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; <strong>if<\/strong> rating &gt; 0:<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; weighted_sum += similarity * rating<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; similarity_sum += similarity<br>&nbsp; &nbsp; &nbsp; &nbsp; <strong>if<\/strong> similarity_sum &gt; 0:<br>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; scores[movie] = weighted_sum \/ similarity_sum<br><br>&nbsp; &nbsp; recommendations = sorted(scores.items(), key=lambda x: x[1], reverse=<strong>True<\/strong>)<br>&nbsp; &nbsp; <strong>return<\/strong> recommendations[:n]<br><br><strong>print<\/strong>(get_recommendations(&#8216;Bob&#8217;, user_item_matrix, similarity_df))<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><strong>Output:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>[(<\/strong><strong>&#8216;Interstellar&#8217;<\/strong><strong>, 3.737&#8230;), (<\/strong><strong>&#8216;Dunkirk&#8217;<\/strong><strong>, 3.618&#8230;)]<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Bob has not seen Interstellar or Dunkirk \u2014 based on his similarity to Alice and Carol, the system recommends Interstellar first.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Common Mistakes Beginners Make<\/strong><\/h2>\n\n\n\n<p><strong>1. Treating zeros as actual ratings<\/strong> \u2014 Unfilled cells in the user-item matrix represent missing data, not zero preference. Filling with zeros is a pragmatic workaround \u2014 but more advanced implementations use matrix factorization to handle sparsity properly.<\/p>\n\n\n\n<p><strong>2. Using too little data<\/strong> \u2014 Cosine similarity breaks down with sparse matrices and few users. Real systems need thousands of interactions before similarity scores become meaningful.<\/p>\n\n\n\n<p><strong>3. Never evaluating the model<\/strong> \u2014 A recommendation system with no evaluation metric is guesswork. Use RMSE for rating prediction or precision@k for ranking quality to measure whether recommendations are actually improving.<\/p>\n\n\n\n<p><strong>4. Ignoring the cold start problem<\/strong> \u2014 New users with no history and new items with no ratings cannot be recommended through collaborative filtering alone. A hybrid approach or onboarding questionnaire is needed to handle cold starts.<\/p>\n\n\n\n<p><strong>5. Skipping normalization<\/strong> \u2014 Users rate differently. One user&#8217;s 3 is another&#8217;s 5. Mean-centering ratings before computing similarity produces more reliable results.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p>Recommendation systems are one of the highest-impact applications of data science \u2014 and Python makes them accessible from day one. Starting with a user-item matrix and cosine similarity covers the core of collaborative filtering without requiring any specialized ML framework. Content-based filtering layers on top cleanly using TF-IDF and the same similarity logic.<\/p>\n\n\n\n<p>The implementation in this guide is deliberately minimal \u2014 production systems add evaluation pipelines, matrix factorization, real-time serving, and A\/B testing on top. But the logic underneath is exactly what was built here.<\/p>\n\n\n\n<p>Master the fundamentals first \u2014 scale comes after.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>FAQ<\/strong><\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1781755629847\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>1. What is a recommendation system in Python?<\/strong>\u00a0<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>A recommendation system in Python is an algorithm \u2014 typically built with pandas, scikit-learn, or specialized libraries like Surprise \u2014 that predicts user preferences and surfaces relevant items based on past behavior or item attributes.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1781755634272\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>2.What is the difference between collaborative and content-based filtering?<\/strong>\u00a0<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Collaborative filtering uses patterns across multiple users&#8217; behavior to generate recommendations. Content-based filtering uses the attributes of items themselves. Collaborative filtering requires no item metadata \u2014 content-based filtering requires no data from other users.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1781755642743\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>3.What libraries are used to build recommendation systems in Python?<\/strong>\u00a0<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>The most common are pandas and NumPy for data handling, scikit-learn for similarity computation, and Surprise or LightFM for more advanced matrix factorization approaches.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1781755651834\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>4.What is the cold start problem in recommendation systems?<\/strong>\u00a0<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>The cold start problem occurs when a new user or new item has no historical data \u2014 making it impossible for collaborative filtering to generate meaningful recommendations. Hybrid systems and onboarding flows are the standard solutions.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1781755663804\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>5.How is recommendation accuracy measured?<\/strong>\u00a0<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Common metrics include RMSE and MAE for rating prediction accuracy, and precision@k and recall@k for ranking quality \u2014 measuring how many of the top-k recommendations were actually relevant to the user.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1781755672410\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>6.Do I need machine learning experience to build a recommendation system?<\/strong>\u00a0<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>No \u2014 the core logic of collaborative filtering is based on similarity math that any Python beginner can implement with pandas and scikit-learn. Advanced techniques like matrix factorization build on these fundamentals.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1781755681815\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>7.What is cosine similarity in recommendation systems?<\/strong>\u00a0<\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Cosine similarity measures the angle between two vectors in multi-dimensional space \u2014 in this context, two users&#8217; rating vectors. A score of 1 means identical preference patterns; 0 means no overlap. It is the standard similarity metric for user-based collaborative filtering.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Quick TL;DR Introduction Every time Netflix suggests a show, Amazon surfaces a product, or Spotify generates a playlist \u2014 a recommendation system is working underneath. These systems are no longer exclusive to tech giants. With Python&#8217;s data science ecosystem, building a functional recommendation engine from scratch is achievable even without a machine learning background. This [&hellip;]<\/p>\n","protected":false},"author":63,"featured_media":117775,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[717],"tags":[],"views":"27","authorinfo":{"name":"Vishalini Devarajan","url":"https:\/\/www.guvi.in\/blog\/author\/vishalini\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/06\/building-recommendation-systems-in-python-300x115.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/117255"}],"collection":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/users\/63"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=117255"}],"version-history":[{"count":3,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/117255\/revisions"}],"predecessor-version":[{"id":117776,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/117255\/revisions\/117776"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/117775"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=117255"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=117255"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=117255"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}