What is Content-based Recommendation System? How Does it Work?
Table of Contents
- What Is Content-based Recommendation System?
- How Does Content-based Recommendation System Work?
- Key Points About Content-based Recommendation System Algorithm
- Uses of Content-based Recommender Systems
- Why Use a Content-based Recommender System?
- Types of Content-based Recommender Systems
- Advantages of Content-based Recommendation Systems
- Limitations of Content-based Recommendation Systems
As we spend a significant amount of our time online now, whether shopping online, watching a movie on OTT, or playing YouTube videos, recommender systems have become crucial for us. Almost every popular media and platform, such as YouTube, Netflix, Amazon, Facebook, etc., uses these recommendation systems to deliver high-quality and personalized content to users, which boosts sales and engagement. This also benefits customers and users as they get suggestions of relevant products and content aligned with their preferences and tastes, which saves them time and effort.
There are two main types of recommendation systems:
In this blog, we will discuss the content-based recommender system.
Content-based filtering uses features and content collected in a system to offer similar recommendations. It fetches the most relevant information from the dataset based on user observations. Myntra, Netflix, Instagram, etc., are common platforms that use content-based recommendation systems.
What Is Content-based Recommendation System?
Content-based filtering recommendation systems leverage machine learning algorithms to find similar suggestions and recommend new items to users. It assigns keywords or attributes to each value in a dataset to recognize them easily. Based on these patterns, the recommendation system shares information about users’ preferences and recommends relevant content or products.
Content-based filtering algorithm suggests products based on their features, which is possible only when there is a clear set of features for products and a list of users’ likes. It stores user data, such as ratings, clicks, likes, and dislikes, to create a user profile. The higher the user engagement, the more accurate the recommendations are.
Let’s take an example. Suppose you are looking for hotels in Delhi near the airport with one night cost around ₹3000. So, the content-based recommendation system will consider two keywords- Delhi and Airport and consider features- distance from the airport and per night cost.
Here is another example explained through a diagram. Suppose there are four movies, and a user has already seen and liked the first two.
Now, the content recommendation system will automatically suggest the third movie instead of the fourth, as the former is similar to the first two. It will calculate similarities based on different features, such as director, actors, duration, genre, etc.
How Does Content-based Recommendation System Work?
Content-based recommendation systems work by analyzing the content and attributes of items (such as movies, books, products, etc.) and matching them with a user's preferences or profile. Here's a step-by-step explanation of how content-based recommendation systems work:
Item Profile Creation:
Each item in the recommendation system is associated with a set of attributes or content features. These attributes can vary depending on the type of items being recommended. For example, in a movie recommendation system, attributes might include genres, actors, directors, and plot keywords.
User Profile Creation:
The system also maintains a user profile for each user, which contains information about their preferences and past interactions. This profile is built over time as the user interacts with the system, providing feedback or indicating their likes and dislikes.
For each item and user, the system extracts relevant features or attributes. For instance, if it's a movie recommendation system, it might extract features like genres, actors, and directors.
Scoring or Weighting Features:
The system assigns weights or scores to each feature based on their importance in predicting user preferences. For example, if a user has shown a strong preference for action movies, the "action" genre feature might be given a higher weight.
Calculating Item Scores:
To recommend items to a user, the system calculates a score for each item by comparing its features to the user's profile. This is often done using mathematical techniques like cosine similarity or Euclidean distance.
Ranking and Filtering:
The items are ranked based on their scores, with higher-scoring items being recommended first. The system may also apply filtering to remove items that the user has already interacted with or items that don't meet certain criteria.
Finally, the system generates a list of recommended items for the user based on the ranked scores. These recommendations are presented to the user through an interface, such as a website or app.
As the user interacts with the recommended items (e.g., watches a movie, buys a product), the system updates the user's profile to reflect their evolving preferences. This feedback loop continuously improves the recommendations over time.
Key Points About Content-based Recommendation System Algorithm
Content-based recommendation systems are personalized because they consider both item attributes and user preferences.
They are based on the assumption that users will prefer items that are similar in content to those they have liked or interacted with in the past.
Content-based systems work well for recommending items with clear and well-defined attributes, such as movies, books, or products.
They are less effective when it comes to recommending items that require a deep understanding of user behavior, such as news articles or social media posts.
Uses of Content-based Recommender Systems
Content-based recommender systems are designed to recommend items to users based on the characteristics or content of the items and the user's preferences. Here are some common uses and applications of content-based recommender systems:
Movie and TV Show Recommendations:
Content-based systems can recommend movies or TV shows to users based on the genre, actors, directors, or plot keywords of the content they have previously liked.
In music streaming platforms, content-based recommender systems can suggest songs or albums based on the user's music taste, taking into account factors like genre, artist, and musical features.
News and Article Recommendations:
Content-based systems can personalize news and article recommendations by considering the content of articles, user interests, and historical reading habits.
E-commerce Product Recommendations:
Online retailers use content-based recommendations to suggest products to shoppers based on their browsing history, past purchases, and the attributes of the products.
Content-based recommender systems in bookstores recommend books to readers based on their preferences for genres, authors, and book descriptions.
Job search platforms can recommend job listings to users based on their skills, work experience, and preferences, ensuring relevant job matches.
Restaurant and Food Recommendations:
Food delivery and restaurant review apps use content-based recommendations to suggest restaurants, dishes, or recipes based on user preferences and dietary restrictions.
Travel websites recommend hotels, flights, and vacation destinations to users based on their past travel history, interests, and budget constraints.
Educational Course Recommendations:
E-learning platforms suggest courses and learning materials to users based on their educational background, interests, and skill level.
Health and Fitness Recommendations:
Health and fitness apps recommend workout routines, meal plans, and wellness tips based on user health goals, fitness level, and dietary preferences.
Content Discovery in Content Streaming Services:
Streaming platforms recommend articles, videos, and other content to users based on their viewing history, content preferences, and engagement patterns.
Real Estate Property Recommendations:
Real estate websites suggest properties to potential buyers or renters based on location, price range, property type, and user preferences.
Art and Image Recommendations:
Art galleries and online art platforms use content-based systems to suggest artworks or images to users based on their artistic preferences.
Fashion and Apparel Recommendations:
Fashion retailers recommend clothing and accessories to customers based on their style preferences, size, and past purchases.
Content Personalization in Social Media:
Social media platforms personalize users' feeds by recommending posts, articles, or videos that align with their interests and engagement history.
Why Use a Content-based Recommender System?
Content recommendation engine maintains privacy and works on purchased, browsed, and past products. It doesn’t require any other personal details from users. As it uses browsed content or products, the features it looks for are the same. Therefore, generating user-based and specific results. So, each user will have a different set of results or suggestions, which will make their experience personalized and unique.
In a nutshell, we can say that the results are unambiguous and candid. Also, as users don’t need to add more inputs or information and the sole parameter for recommendations is attributes, the results are relevant and transparent.
Another benefit of using content-based filtering is that it is uncomplicated to use and easier to build.
Types of Content-based Recommender Systems
Content Filtering Using Item Data
Item-based content recommendation system evaluates each feature of an item in the matrix and suggests items accordingly. Pandas, a Python library, helps calculate the matrix values for recommendations. Here, products considered for suggestions are crossed with products browsed based on their similarities.
According to the rating of the browsed products, the model renders a rating for other products and foreshadows their ratings. Items with a higher predicted rating are recommended to users.
Content Filtering Using User Data
If we add a few attributes along with browsed products, purchase history, and purchased products, the system can provide us with more customized results by using user data. Therefore, building profiles is a great way to save data to be used later. In content-based filtering algorithms, the focus is more on the common attributes of several items instead of the ones that are not common.
As all the attributes of an object are not identical to users, it increases its importance. Websites and apps often request users to rate their services and products because reviews are crucial while evaluating items. We can say that content-based filtering using user data uses saved data or inputs to produce recommendation results. However, the downside of this method is it offers a lack of data privacy to generate accurate results.
Advantages of Content-based Recommendation Systems
It is easy to use and provides accurate results to every user.
It suggests even new items as soon as they are launched without census as features are available from the start.
As the content recommendation engine understands the underlying features, there is no need for any intervention or input.
As recommendations are based on daily user activities, parameters and preferences of suggestions are aligned to a user’s choice. The content-based algorithm also recommends niche items that other users might not find interesting.
It is the best filtering system when considering a group of products belonging to a single type.
It can be scaled easily to match a large number of users because it doesn’t need data from other users to recommend items to a particular user.
Limitations of Content-based Recommendation Systems
As it recommends products based on the present interests of users, it is not pssible to expand and discover newer avenues that users find interesting.
Classifying new products while adding them can be tedious.
It is difficult to recommend users who are not that active.
To build a content-based recommender system, you need extensive domain knowledge, as the feature selection of items is hard-coded into the system. Therefore, the content recommendation platform is only as good as the knowledge of the developer building the model.
As products are almost similar to each other, there is hardly any diversity of products in small datasets, which makes the result redundant.
As it calculates the product of items to find similarities, the method can fail if there are missing values in the dataset.