Integrant’s Java Recommendation Engine

While shopping online, have you ever wondered how Amazon knows your favorite books, makeup, or shoes? Amazon uses a recommendation engine (typically with machine learning or artificial intelligence) to suggest products that it thinks you would be interested in buying.
In other words, “Product recommendation is basically a filtering system that seeks to predict and show the items that a user would like to purchase. It may not be entirely accurate, but if it shows you what you like then it is doing its job right.”
Recommendation engines have become increasingly popular in recent years and are often used to recommend movies, music, news, books, and other products. They’re also utilized to suggest research articles, search queries, and social tags.

While many of our customers have a B2C component, there are still many uses for recommendations outside of e-commerce platforms. For example, imagine your company has a large product catalog, you can use a recommendation engine to help your sales team find additional services or products to suggest.

With the versatility and potential impact of using a recommendation engine internally or externally, what better way to showcase our team’s Java skills than to build our very own version. In the remainder of this article, we’ll describe some of the supporting technologies we used to build our Java-based recommendation engine and key decisions we’ve made so far.

However, Java contains many strengths as well. Java is a general-purpose programming language that contains a multitude of possible integrations and applications. Java is very stable and has lots of resources, libraries, and frameworks, and it has an extremely robust and long-established user community.

In addition, Java is hard to beat for end-over-end value in building complex enterprise applications. Java is a true workhorse throughout the industry, and it’s maintained an excellent reputation for many years.

Another important factor influencing our decision, and perhaps the most important for our purposes, is that the client’s project was based in Java. Obviously, we were biased in that direction.

Furthermore, we wanted to demonstrate that Java is a great language for building recommendation engines, and as we’re discovering, Java is second-to-none and works every bit as good, if not better, than Python as a recommendation engine code base.

After debate amongst our team members, we chose PostgreSQL for our data storage solution. Since complex relationships exist between the client’s product categories and the specifications for each category, we determined that a relational database was well-suited for this project.

A document-based database would require additional processing to search documents to find the ones that included the right product information, and it would then have to update that information accordingly. Since PostgreSQL has a great reputation for reliability, diversity of features, and performance, it was an easy choice. Plus, the database is free.

While building our engine, we had to choose between two different approaches to filtering data: collaborative filtering or user segmentation. Collaborative filtering works by comparing a large group of people to find users that share similar tastes with another user. It compares the items they like and combines them to create a ranked list of suggestions. This approach requires integration with AI and a machine-learning approach called “Deep Learning.”

We eventually decided to use user segmentation as our filtering approach. User segmentation identifies user interests based on unique characteristics, needs, preferences, and tastes. Each user interest is assigned a particular “weight,” which is then used to calculate a recommendation.

For instance, in the context of movie-watching, a user’s watch action has more value than simply adding a movie to their watchlist. Although someone may add a movie to their watchlist 100 times, the movie will still be discarded from future recommendations if they only watch it once.

For our client’s product catalog, we will segment based on user interest, so we can show the right product recommendations at the right time. While this approach doesn’t require AI integration, we’d like to eventually adopt a more AI-centric approach to train our applications and improve the engine with the latest machine-learning technologies.

Another question that we had to answer was whether to make our engine generic or customized. Our original vision was to build a solution that would be focused specifically on our client’s product catalog.

As things progressed, it became apparent we should be more generic in our approach. By doing so, we’d learn more in a short amount of time by not being locked into a specific industry-focused solution. Therefore, we decided to adopt an already available movie database as our testing ground. This strategy would allow us to extend our learnings across multiple industries as opposed to focusing on just one specific industry.

Recommendation engines are prevalent in most industries today, so every tech savvy business owner, especially those in the ecommerce industry, should consider adding one to their company website.   When we first agreed to build a recommendation engine for our client, we weren’t sure how to approach the task. We had many questions to answer and even more questions arose during our research. We had to determine the best language and database to build the product with, how to perform filtering, and even more challenges to solve.   We were faced with a difficult task, but we were more than ready for the challenge. Everyone on the Integrant development team is passionately committed to delivering the highest quality results for clients. If we don’t have the answer, we’ll search relentlessly until we find it. Whether building a new site from scratch, or migrating a legacy application, we are Java “ninjas” who thrive when solving challenges.   As a result of our research findings, we determined that Java is a natural fit for this project. We’ve made significant progress and are nearing the project’s demo phase.   If you have a business challenge that you need help with, we can help. If you’re looking for seasoned enterprise Java developers, then you’ve come to the right place. We’d love to work with you, so give us a call today to learn how we can partner together on your next project.

Building a Recommendation Engine in Java

Why We Chose Java, Other Technologies that Made the Cut, and Our Starting Methodology

Non-E-Commerce Uses for a Recommendation Engine

For more insights, check out our case studies

Supporting Technologies

Java vs. Python

If you’re curious about additional services, check out what we can offer you!

Frontend: Angular 7 & Bootstrap

Data storage: PostgreSQL

Database: MongoDB

Containerization: Docker

Kaggle

TensorFlow

Collaborative Filtering vs. Segmenting Users

We implement the right code for the job. Best practices for new features from Java

Generic or Customized?

Next Steps

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