In the last few years, recommendation systems have become vital tools for increasing user engagement. Digital platforms implement these solutions to suggest products and content to their visitors based on previous interactions and things they liked or disliked. By tapping into large datasets available online, they can determine user preferences, thus simplifying buyers’ journeys and customer satisfaction.
Some of the best platforms in the world rely on these systems, including Amazon, YouTube, and Netflix. Given the nature of recommendation systems, they require sophisticated databases that will tie everything together. In this article, we’ll talk about this technology and the best databases you can use to build it.
Why Do Users Need Real-Time Recommendations?
Real-time recommendations have become crucial for enhancing our web browsing experience. We get things when we want them, the way we want them, without having to endure useless ads, videos, and product recommendations. Most importantly, real-time recommendations adapt to any change in our behavior, ensuring that the suggestions remain relevant over time.
Perhaps the best example of this is YouTube’s algorithm. The platform reviews your previous watch data to provide suggestions according to your interests. If you ever switch to other topics, the algorithm will take note of that shift, changing suggestions across the board.
Real-time recommendations provide value to everyone involved. Users can see products, ads, and content they’re interested in, reducing the time necessary to browse. On the other hand, platforms that provide better suggestions will benefit from increased revenues or increased user retention.
What Is the Best Database Type for Recommendations?
According to numerous practical examples, graph databases are ideal for creating real-time recommendation engines. These systems excel at uncovering relationships between data points and specific patterns. Among others, graph databases can detect the following behavior:
- Behavior where users buy similar products and services over a longer time span
- Situations where users browse the same keywords online
- Users looking at a specific group of items but buying different products
- Situations where a buyer bought and afterward recommended the product to friends
- Following similar channels and groups on social media
Whatever the case might be, graph databases can uncover all sorts of behaviors that might seem erratic to other systems. While you might achieve similar results with other software, graph solutions will always outshine the alternatives as they’re much better at connecting the dots.
4 Best Graph Databases for Real-Time Recommendations
In theory, you can use just about any graph database to create real-time recommendation systems. However, based on our empiric knowledge, these solutions will provide the most value out of the bunch:
1. Neo4j
If you’re looking for a reliable database solution, you should definitely consider Neo4j. Touted as one of the best native graph databases, the software is fully ACID compliant and provides fantastic scaling. Neo4j uses cypher query language, clustering, graph algorithms, and visualization.
Most companies praise Neo4j for being robust and reliable. On top of the previously mentioned scalability and clustering, it also has excellent security, ensuring that your data is always protected. With this graph database, you can easily traverse between nodes and discover patterns that weren’t previously noticeable, making it a great choice for recommendation systems.
2. NebulaGraph
If you’re looking for a perfect graph database for building a recommendation engine, look no further than NebulaGraph. The open-source solution is an excellent choice if you have to process large quantities of data while dealing with minimal latency. Besides detecting relevant patterns at a high speed, NebulaGraph is popular for its analytics features.
Besides recommendation systems, this digital solution can provide enormous value for security, social media, capital flows, knowledge graphs, and AI. The system is renowned for its fantastic product and content recommendations, as well as content personalization. All these features are fantastic for online stores, as they allow businesses to capitalize on accrued user data.
3. TigerGraph
Perhaps the best thing about TigerGraph is its speed. It can take care of complex graph workloads in the shortest amount of time, providing an excellent experience to all its users. That makes it an ideal option for specific use cases where users require immediate insights.
We’d also like to mention that TigerGraph uses TigerGraph Query Language or GSQL. With it, you can create parallel, distributed graph databases that also include comprehensive real-time analytics functionality. The graph database also provides support for GRPC, GSQL, and ODBC.
4. Dgraph
Dgraph shines when tackling large datasets as well as distributed environments. The open-source solution is horizontally scalable and relies on Dgraph Query Language. With this system, you can perform speedy queries by tapping into data stored in distributed clusters.
If we also consider the fact that the graph database can tackle complex traversals, it becomes obvious that it’s a great solution for recommendation systems. Among other things, users can enjoy consistency across clusters as well as low-latency queries. All of these features make it perfect for large-scale graph applications.
Conclusion
Even if you prefer some other type of database for creating recommendation engines, graph solutions remain the best option at your disposal. These databases shine at uncovering patterns within large datasets by quickly traversing complex queries.
Most graph databases, like NebulaGraph, also have fantastic scaling potential. They provide the best results when analyzing large quantities of data and connecting numerous nodes. Graph databases are generally fast (especially when compared to some other types of databases) and have excellent real-time analytics features.