What is a recommendation system?

A recommendation system is rapidly becoming a major component and is specifically designed for any app such as social, e-commerce, or educational. The main purpose of a recommendation system is to provide a list of suggestions based on specific criteria. These may vary depending on the nature of the application, for example, user's interest, search history patterns, geographical location, etc.

An example

The recommendation system in an education application assists users in selecting the best course from a wide range of choices by suggesting relevant courses based on their requirements and the course's popularity.

How a recommendation system works

Generally, a recommendation system works in the following steps:

1. Data collection: Gather relevant data related to the products or users. This data can include users’ profile information, their search patterns, product details, and number of likes/dislikes on products, etc. For example, YouTube collects user data such as watch history, clicks, likes, shares, etc. to recommend relevant videos to users.

2. Data preprocessing: After collecting the data, we need to store and preprocess it. This step handles such entries in the data that have missing, duplicates, or misleading values. We can perform preprocessing using data mining or machine learning tools.

3. Feature selection: We also have to focus on selecting the relevant features from the collected data that help the recommendation system to generate accurate suggestions. For example, user engagement elements (such as comments, shares, likes/dislikes, etc.), or user preferences can be helpful in generating relevant suggestions.

4. Recommendation algorithm: Choosing the algorithm is an important step in generating relevant recommendations based on the available data. We have multiple recommendation algorithms, for example, collaborative, content-based, matrix factorization, etc.

5. Evaluation and deployment: Evaluate the performance of recommendation algorithms using various evaluation metrics. If the algorithm performs well, the recommender system is considered ready for deployment in the application. Otherwise, parameter tuning and feature selection enhancement may be required to improve its performance.

Benefits of recommendation systems

Recommendation systems offer many benefits to applications. Let's look at some of them in the table below:

Conclusion

A recommendation system is a powerful system that uses data mining or machine learning approaches to provide personalized content. It helps users find relevant products and ultimately assists businesses in satisfying customers and achieving their goals.