How to build a e-commerce recommendation system

In today’s e-commerce providing a personalized shopping experience to users is crucial.

There is a way to achieve this by implementing a product recommendation system and I will show you how to build a recommendation system for e-commerce.

A recommendation system helps customers discover products they’re likely to buy based on their browsing behavior, previous purchases, similar products, and even what similar users are interested in.

What is a Recommendation System?

Recommendation system is a tool that analyzes user data to suggest products or services. It can help users to find relevant products, which enhance the user satisfaction.

Whether you’ve noticed product suggestions while browsing Amazon or movie recommendations on Netflix, these are all powered by recommendation engines.

Why Do You Need a Recommendation System in E-commerce?

Personalised Shopping

Recommendation system offers or suggests the products to customers according to their preferences. Which makes their shopping experience smoother and enjoyable.

Boost Sales

A recommendation engine can increases the purchases by showing the right products at the right time.

Customer Retention:

When using recommendation engine it often lead to higher engagement, encouraging customers to return and continue shopping.

Steps to Building an E-commerce Recommendation System

1. Collect the Right Data

The quality of your recommendation system depends heavily on the data you collect. It includes user’s behavior, product details, and past interactions.

Collecting more data, the better your system can be at making accurate predictions.

Here’s a breakdown of the key data types:

• User Data: Like browsing history, past orders, wishlist’s and demographics (age, gender, location).
• Product Data: Product categories, descriptions, pricing and images.
• Interaction Data: Like how users engage with products (e.g., clicking, adding to cart, viewing).

For e.g, tracking the customer- how many times a customer views a product can indicate interest and inform future recommendations.

2. Select a Recommendation Approach

There are three main types of recommendation systems you can choose from:

• Collaborative Filtering: This method looks at the behavior of similar users. If two customers have shown similar preferences, the system will suggest products to one based on what the other has interacted with.
• Content-Based Filtering: Here, the system recommends items similar to ones a user has shown interest in by analyzing product features like descriptions and categories.
• Hybrid Systems: In this method, the system recommends items similar to ones a user has shown interest in by analysing product features like descriptions and categories or similar products.

For e.g, if User A has bought similar items as User B, collaborative filtering will suggest products that User A purchased, but User B has yet to discover.

3. Prepare Your Data

Before you can build a recommendation system, you need to prepare the data. This process involves:

• Cleaning the Data: We have to removing duplicates, filling in missing information, and standardizing formats from data.
• Normalizing Data: We have to ensuring that different data points (like ratings or views) are on the same scale to improve model performance.
• Feature Engineering: Now creating some additional insights, such as product popularity or calculating the time users spend on certain products which help in find similar users.

For instance, The number of times a product has been viewed or how often a user adds items to their cart should be calculated, so this can help fine-tune recommendations.

4. Build Your Recommendation Model

There you will see, how to build a recommendation system. According to the approach you’ve chosen (Collaborative, Content-Based, or Hybrid), you’ll use different algorithms:

• Collaborative Filtering: There we use techniques such as Matrix Factorization and k-nearest neighbors (k-NN).
• Content-Based Filtering: There we use algorithms like cosine similarity or TF-IDF to compare product attributes. Also, we use vector database for content-based filtering.

Collaborative Filtering

We first have to collect the user’s data, like user product rating, likes-dislikes, preferences etc.

Then we use Matrix Factorization or clusters using kNN to make a similarity table to find similar users and suggest products accordingly.

For example: (Matrix Factorization) This is the users and product rating processed pivot table which contains each user rating for each movie in the 0 – 5.0 range.

The table contains 0.0 for that user who hasn’t rated the product yet.

We convert this pivot table into a user similarity table, this table shows how users are similar with different users.

Now we get the user’s similarity matrix table. Through this table, we can predict ratings for those users who have not given ratings yet.

User no 64 and 115 have 0.663 or 66.3% are similar so we can suggest products if user 64 buys a new bag then the recommendation system suggests the same product to user 115.

Product Content-Based Filtering

We first generate the embeddings of the product title, description, and price and save them in a vector database.

We can use TFIDF or Word2Vec or Transformers or any hosted API’s (like openai and cohere) for creating embeddings.

Now we can suggest similar products, for e.g similar tastes, patterns, attributes, etc.

We have a Magento 2 Product Recommendation System, which works on content-based filtering. It shows related or similar products by our recommendation system.

Conclusion

A recommendation system is a powerful tool that can enhance your e-commerce platform user experience and increase sales.

It can improve customer satisfaction and retention through personalized product suggestions.

Building a recommendation system requires a mix of data collection, the right algorithm, and the right optimization.