Product Matching Using Machine Learning

product matching

What does the product look like?

The first step of any ML project is to identify what features we want our model to focus on. We use these features to train our machine learning algorithm. Features can be anything that helps us understand the data we have gathered. In this case, we will be using the image of the product to determine if we should match it with a specific product.

How do I gather my data?

For this project, we will be gathering product images and their corresponding labels. These images will be fed into our model and then trained to recognize patterns in the data. Once we have enough training data, we can start building our model.

1. Amazon product search

Amazon’s product search page is pretty simple. You enter some keywords related to what you’re looking for and it returns a list of results. If you click on any result, it takes you to the product details page. On the left-hand side, you’ll see three tabs: ‘All’, ‘Similar’, and ‘Customers who bought this item also bought. Clicking on these three tabs shows you the top 10 items based on how closely they match the keyword you entered.

2. Similar

Click on the similar tab, and you’ll get a list of products that look like yours. These are the ones that are closest matches to your original query.

3. Customers who bought this item also purchased

This is where you’ll find out if anyone else has bought the same thing. If someone else has bought the same product, then it means that the two products are probably a good fit.

4. Customers who viewed this item also viewed

If you click on this tab, you’ll get a list of people who looked at this particular product. If you click on a person’s name, you’ll go straight to their profile page.

5. Products that customers bought together

You can use this feature to see if anyone else has bought a combination of products that you’ve got. So if you’ve got a dresser, bedside table, and lamp, you could check if anyone else has bought those products together.

6. Related searches

This is where you can see what people searched for after searching for something similar to your product. If you click a link, you’ll go to a page showing the top 10 results for that search term.

What type of model am I going to use?

In order to make sure we get good results, we will need to choose a good model. There is no single best algorithm for everything, so we will need to find the right balance between accuracy and speed. We will use convolutional neural networks (CNN) to build our model. CNN’s are great at identifying patterns in data.

How do I train my model?

Once we have chosen our model, we need to figure out how to train it. We will use Tensorflow to do this.

How do I test my model?

We will be testing our product matching model by feeding it a set of products and seeing how well it matches them. If we get a high percentage of correct matches, we will know that our model worked!


About Author

Greetings, I am Pankaj Pandey, a dedicated professional with eight years of experience in the dynamic field of digital marketing. My journey in this ever-evolving landscape began with a genuine passion for exploring the vast potential that digital platforms offer for brand promotion and customer engagement. Specializing in SEO, social media marketing, content strategy, and online advertising, I have cultivated a strategic mindset and innovative approach that sets me apart. My campaigns have consistently increased brand visibility and delivered measurable results, showcasing my adaptability to industry trends and technological advancements. As the site owner and author of, I combine my digital marketing expertise with insightful content creation. This platform reflects my commitment to sharing knowledge, providing valuable insights, and guiding businesses towards success in the digital era. My journey is a testament to my unwavering commitment to excellence and innovation in the digital marketing realm. I look forward to continuing to push the boundaries of what is possible in the ever-evolving world of online promotion.

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