Recommender Systems

By: Katana Graph

May 13, 2022

Recommender Systems

As product availability becomes vaster and more varied, consumers want simpler shopping experiences.

Notably, the way Amazon presents its recommendations has shifted from their earlier assertion of “You might also enjoy…” to “Customers who searched for X ultimately bought…” Transforming the preference list of individuals into a preference list of the collective comes with the implication that if enough people like something, you will too — as opposed to their earlier version where your suggestions were based on the purchases made by customers judged similar to you. Algorithmic recommender systems are usually designed to calculate a product’s overall relevance, offering a personalized recommendation. Most of us are familiar with Netflix’s Up Next suggestions or Spotify’s Daily Mixes, which categorize media by genre and style and categorize populations by their taste in both.

Using recommendation systems to provide personalized content or search experiences is an established way for marketers to reach their desired audience, but there are challenges surrounding their efficacy. For example, these systems typically fail to give good recommendations to new users because the system doesn’t have enough information about them. To solve this, several platforms began implementing onboarding processes that survey a user's interests before directing them to the news feed.

Information filtering systems must consider audience preferences in addition to product categories before making a recommendation. On a basic level, through content-based filtering, these systems associate a user’s viewed content with databases of product information in an attempt to understand what the user wants to see. This model requires only one user’s data and is relatively scalable but has limited ability to expand beyond a user’s existing interests. 

Collaborative-based filtering studies users with similar profiles and makes suggestions based on a group’s predicted interest in a product. As online shopping becomes more prevalent and online retailers are growing, the huge amounts of data being produced makes it time-consuming to mine for the details needed for information filtering systems. Making recommendations that buyers actually welcome now requires correlating customer information, product details, product inventory, supply chain information, and sentiments expressed on social media. Graph databases serve advanced recommendation systems well, for example, by constructing co-purchased product clusters in which nodes represent the products and edges represent the purchase of products by customers. In other recommender systems, the relationship between users and their ratings is represented as a bipartite graph (a graph whose vertices can be divided into two disjoint and independent sets) where users and products are different types of nodes and labeled edges are the ratings of products by their buyers.

It eases challenges surrounding the timeliness of batch processing by prioritizing relationships between entities rather than focusing solely on customer touchpoints. Basically, if a customer puts shoes in their cart, the system will suggest socks.

In addition to the difficulties surrounding efficient personalization of a consumer’s experience, data input, preparation, and processing are also affected by the volume of data being produced. The resources and tools required for large-scale data aren’t a new limiting factor, but they struggle to keep up with the pace of data.

With the acceleration of data production expected to increase every year, the volume of data also forces businesses to choose between thorough and fast analytics. By combining AI/ML and graph technology, Katana Graph helps businesses understand their data on a deeper level with elevated query, mining, and analytics with their graph intelligence platform.

Katana Graph provides graph AI with graph neural networks (GNN), a new powerful approach for feature learning on graphs. Katana Graph created easy-to-use and scale-out packages for learning large-scale knowledge graph embeddings, essential applications such as recommendation systems, node classification, and link prediction used for supply chain management, bioinformatics, and cheminformatics.

Whether structured or unstructured data, Katana Graph can greatly improve the insights and opportunities uncovered from your organization’s data. Click to schedule a meeting.

Get to Know Katana Graph

We thrive on testing new and diverse ideas.

Katana Graph was born of cutting-edge research and scientific rigor, and these beginnings have a powerful effect on who we are to this day. We’re devoted to problem solving, and are relentless in our pursuit of more effective and more efficient solutions to real-world challenges. Continuous improvement is the very foundation of our success.


Newsletter Sign Up

Optimizing Large-Scale Distributed Graph Neural Networks on Intel CPUs

Training Graph Neural Networks on Large Graphs Graph neural networks (GNNs) are a powerful tool for.

Read More
Rethinking Buyer Behavior Algorithms

To standard traffic analyzers, one click is as good as another. Our impulse purchases and our most.

Read More
Katana Graph’s Analytics Python Library

As businesses grow and face increasing data challenges, they must find ways to tackle more.

Read More

View All Resources

Let’s Talk

Turn Your Unmanageable
Data Into Answers

Find out how Katana Graph can help provide the foundation for your future of data-driven innovation.

Contact Sales