A Beginners Guide to Recommender Systems and How They Work


A recommender system is a technology that uses machine learning and data analysis to match people with similar tastes and preferences.

A user may have many followers on social media, but it’s likely they only follow a small handful of users in real life. So when a user signs up for an app or website, their posts are less likely to be looked at by others unless they are recommended by friends and family.

An online retailer may recommend other products that its users tend to buy together as well as other items based on a user’s previous purchases.

A recommender system helps you find people who like the things your users like so that you can target them more effectively with future marketing campaigns or advertising. In this article, we’ll explore what a recommender system is and how it works.


What is a Recommender System?

A recommender system is a software application that analyses user behavior and preferences to recommend products and services to customers. Nowadays, most e-commerce websites use recommender systems to suggest other products that a customer has shown interest in.

For example, you are browsing online clothes stores and you like one of their items. You may be asked to share your views with the brand via social media to let them know that you would like to purchase it. The online store that you are interested in can then use the data you have shared with it to recommend other products that you might be interested in.

A recommender system uses data from users and from external sources to create predictive models that can understand users and their preferences. These models can then recommend similar products to users so that they can get what they want.


The Advantages of Using a Recommendation System

Better Targeting: You no longer need to waste time reaching out to everyone in the world because a recommender system can focus on a specific audience, like a niche market.

Low Cost: Unlike email marketing, where you need to invest a lot of money in creating, re-creating, and re-targeting your content with different email templates, a recommender system only requires a couple of data points.

Higher Engagement: Users are more likely to be engaged if they are shown products that they may be interested in. This is especially useful when you’re selling physical products.

Better Customer Retention: If you have a product that appeals to a specific type of person, you can use a recommender system to target those people and keep them as customers.

Increased Conversion Rate: A recommender system can help you increase your conversion rate by showing you content that your users are likely to like and click on the content. You can show the user relevant ads or recommendations at the right time, increasing your revenue.


Some of the Applications of the Recommender Systems

– Amazon used a product recommendation system for its customers to show similar products,

– Content recommenders for social media platforms such as Facebook, Twitter, and Instagram,

– Used as playlist generators for video and music services like Netflix, Youtube, Spotify, and many more…


Different Sources for Recommendation System

– Browsing and searching Data

– Purchase data

– Feedback explicitly provided by the users

– Textual comments

– Expert recommendations

– Demographic data


Types of Recommenders

There are three main types of recommender systems:

1. Content-based Filtering

2. Collaborative Filtering

3. Collaborative Filtering

Types of Recommender System

Content-based Filtering

This type of system focuses on the similarity attribute of the items to give recommendations. Content-based filtering methods are based on a description of the item and a profile of the user’s preferences.

These methods are best suited to situations where there is known data on an item (name, location, description, etc.), but not on the user.

Content-based recommenders treat recommendation as a user-specific classification problem and learn a classifier for the user’s likes and dislikes based on an item’s features.


How Does Content-Based Recommendation System Works?

Step 1: Item Profiles
In a content-based system, we must construct for each item a profile, which is a record or collection of records representing essential characteristics of that item. For Example, Movie: features are actors, director, genre, and year in which the movie was made. Based on these features similar movies will be recommended.

For Documents like news articles, we use distance measure as a measure of Similarity, namely – Jaccard Distance and Cosine Distance, and concepts of Term Frequency and Inverse Document frequency(TFIDF) is used. For Images, Tag words are used as words or phrases that describe the image item.


Step 2: User Profiles
We must create vectors with the same components describing the user’s preferences. We have the utility matrix representing the connection between users and items.


Step 3: Recommending Items based on content
With profile vectors for both users and items, we can estimate the degree to which a user would prefer an item by computing the cosine distance between the user’s and the item’s vectors.

Content based filtering

Pros & Cons of Content-based Filtering


– This type of filtering is user independent.

– Support transparency.



– Limited content analysis.

– This type of filtering is not suitable for new users.


Collaborative Filtering

Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future, and that they will like similar kinds of items as they liked in the past.

It focuses on the relationship between users and items. The similarity of items is determined by the similarity of the ratings of those items by the user who rated both items.

It consists of two main entities:
i) User: Any individual who provides ratings to a system
ii) Items: Anything for which a human can provide a rating


Collaborative Filtering Methods are Classified into Two Types:

1) Memory-based approach

The memory-based approach uses user rating data to compute the similarity between users or items. Typical examples of this approach are neighborhood-based CF and item-based/user-based top-N recommendations.


2) Model-based approach

In this approach, models are developed using different data mining, and machine learning algorithms to predict users’ ratings of unrated items.

There are many model-based CF algorithms likewise Bayesian networks, clustering models, latent semantic models such as singular value decomposition, probabilistic latent semantic analysis, multiple multiplicative factors, latent Dirichlet allocation, and Markov decision process-based models.

collaborative filtering

Collaborative Filtering Examples


Pros & Cons of Collaborating Filtering


– Works for any kind of item because there is no feature selection needed.



– Cold start: For a new user or item, there isn’t enough data to make accurate recommendations.

– Scalability: In many of the environments in which these systems make recommendations, there are millions of users and products. Thus, a large amount of computation power is often necessary to calculate recommendations.

– Sparsity: The number of items sold on major e-commerce sites is extremely large. The most active users will only have rated a small subset of the overall database. Thus, even the most popular items have very few ratings.


Hybrid Filtering

Combine techniques of Content-based Filtering and Collaborative Filtering and avoid some of the shortcomings. It generates candidate information sets using content-based filtering. The candidates are ranked based on collaborative filtering.

Basically, it tackles the problem of a cold start.

Hybrid filtering combiner

Example of Hybrid Filtering
Netflix makes recommendations by comparing the searching habits of similar users (collaborative filtering) as well as offering movies that share characteristics with firms that a user has rated highly (content-based filtering).

hybrid filtering example



Here in this post, we simply start with the introduction part of recommender systems digging into advantages and applications in the real world. We also go through the different types of recommender systems and how they are actually working.

The recommender system isn’t always right, but it’s usually very accurate and can improve with more data. The team of researchers is further studying its applicability which could be a boon for social marketing and low-cost advertisements.

I Hope I’ve Shown You all the necessary details for the recommender systems that the novice should know while entering this field. If you’ve any doubts, reach out to us:-)

Have a nice day!


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