Solutions: Real-Time Recommendation Engines
Real-time recommendation engines are key to the success of any online business. To make relevant recommendations in real time requires the ability to correlate product, customer, inventory, supplier, logistics and even social sentiment data. Moreover, a real-time recommendation engine requires the ability to instantly capture any new interests shown in the customer’s’ current visit – something that batch processing can’t accomplish. Matching historical and session data is trivial for a graph database like Neo4j.
The key technology in enabling real-time recommendations is the graph database, a technology that is fast leaving traditional relational databases behind. Graph databases easily outperform relational and other NoSQL data stores for connecting masses of buyer and product data (and connected data in general) to gain insight into customer needs and product trends.
Shaping Up a Fitness Program Recommendation Engine & More
See how Benjamin Nussbaum builds a personalized recommendation engine for BeachBody fitness programs, nutritional supplements and more.Read more →
Powering Recommendations with a Graph Database
Learn how companies like eBay and Walmart are using graph databases to power their real-time recommendation engines.Download the white paper →
Webinar: Product Recommendations with MongoDB and Neo4j
Watch how MongoDB can be used to provide search and browsing functionality for a product catalog while using Neo4j to provide personalized product recommendations.Watch the webinar →
Whether you’re leveraging declared social connections or connecting the dots between seemingly unrelated facts to infer interests, graphs offer a world of fresh possibility when it comes to making better real-time recommendations for your users. Connect people to products, services, information or other people based on their user profile, preferences and past online activity such as product purchases.
Enable users to search for products, services or people based on a host of fine-grained criteria and continually improve recommendations by accommodating new data sources and types – without an intensive re-write of your data model.
Whether the recommendation engine uses collaborative- or content-based filtering, it needs to traverse a continually growing, highly interconnected dataset.
The power of a recommender system lies in its ability to make a recommendation in real time employing users’ immediate history. However, traversing a complex and highly interconnected dataset to provide contextual insights is a challenge without the right technology.
The accuracy and the scope of recommendations increase as you add more nodes or data points. The rapid growth in the size and number of data elements means the suggestion system needs to accommodate both current and future requirements.
Unlike relational databases, Neo4j stores interconnected user and purchase data that is neither purely linear nor hierarchical. Neo4j’s native graph storage architecture makes it easier to decipher suggestion data by not forcing intermediate indexing at every turn.
Neo4j’s versatile property graph model makes it easier for organizations to evolve real-time recommendation engines as data types and sources change.
Neo4j’s native graph processing engine supports high-performance graph queries on large user datasets to enable real-time decision making.
The built-in, high-availability features of Neo4j ensure your user data is always available to your mission-critical recommendation engine.
Real-time analysis of data relationships is essential to uncovering fraud rings and other sophisticated scams before fraudsters and criminals cause lasting damage.
Queries: Anti Money Laundering (AML), Ecommerce Fraud, First-Party Bank Fraud, Insurance Fraud, Link Analysis
Tap into the power of graph-based search tools for better digital asset management using the most flexible and scalable solution on the market.
Queries: Asset Management, Cataloging, Content Management, Inventory, Work Flow Processes
Graph databases are inherently more suitable than RDBMS for making sense of complex interdependencies central to managing networks and IT infrastructure.
Queries: Asset Management, Cybersecurity, Impact Analysis, Quality-of-Service Mapping, Root Cause Analysis