Empowering Recommendation Engines with Advanced Algorithms for Personalized Suggestions
Bitnine Global Marketing team
Fri Jul 05 2024
In today's data-driven world, personalized recommendations are essential for enhancing user experiences on various platforms. Businesses strive to stand out by providing tailored suggestions that truly resonate with their customers.
This is where recommendation engines equipped with advanced algorithms come into play. In this article, we'll explore the mechanics behind Hybrid Graphs and how they can help your business understand the crucial processes of data analysis, management, and visualization.
What is a Hybrid Graph?
A Hybrid Graph combines the advantages of property graphs and hypergraphs.
Property Graphs label characteristics of nodes and edges, making it easy to understand the relationships between objects.
Hypergraphs consist of nodes and hyper-edges, which represent common properties by connecting multiple nodes into a single hyper-edge.
By merging these two types, Hybrid Graphs enable intuitive analysis of relationships and common properties, which helps in defining clusters and deriving valuable insights.
Utilizing Hybrid Graphs with Bitnine Global
Bitnine Global employs graph query modeling alongside the traditional relational model. It supports SQL syntax, allowing seamless querying and exploration of relational databases.
Figure 1. Property Graphs and Hypergraphs in matrix form
Figure 1 illustrates the difference between property graphs and hypergraphs. In a property graph, connections between nodes are shown as 0 or 1, indicating the presence or absence of a relationship. In contrast, a hypergraph highlights common properties through elements called hyperedges, labeled here as 1, 2, and 3. Matrices in hypergraphs display the presence or absence of these hyperedges with 0 or 1. For instance, nodes A and B are connected by Hyperedge 1, while nodes A, C, D, and E are connected by Hyperedge 2.
Property graphs represent one-to-one relationships between connected nodes, while hypergraphs depict one-to-many relationships via hyperedges, offering a more integrated representation of data relationships. Each type of graph has its own strengths and weaknesses, making them suitable for different purposes. This is where Hybrid Graphs come into play, combining the best features of both property graphs and hypergraphs to provide versatile and comprehensive data analysis.
Hybrid Graph Utilization Value
Data Analysis: Hybrid graphs help analyze related data by visualizing complex relationships, identifying patterns, and understanding customer behavior
Communication: They enhance internal and external communication by visualizing cause-and-effect relationships through property graphs and abstract concepts through hypergraphs
Data Management Efficiency: Hypergraphs improve metadata management by excluding unnecessary information and organizing data based on common characteristics
Example of Hybrid Graph Application
Figure 2. Graph Modeling for relationship Analytics
Imagine we have a graph where users, items (e.g., clothes and accessories), and brands are represented as nodes. The edges show the connections between users and items through ratings and the links between items and brands.
This graph structure allows us to examine how users rate specific items and how those items are associated with particular brands. By analyzing this data, we can gain insights into customer preferences, popular brands, and the overall sentiment toward different items.
For instance, let's say we have fashion review data from a shopping mall. In our analysis, we design the graph structure as shown in Figure 2. The nodes consist of three types: user, item, and brand. The edges represent the ratings given by users to items and the relationships between items and their manufacturing brands.
Figure 3. Discover the indirect interaction between users using graph structure visualization
Figure 3 illustrates a property graph representing items and user rating behavior. Through property graphs, you can identify users with similar interactions on particular items. This allows you to discover new relationships through common attributes, even when there is no direct relationship history between users, but rather indirect interactions with items.
The designed graph model includes use-item-brand nodes and their relationships. Users are represented as nodes, while outlined hyperedges represent brands and the characteristics of items. This setup enables the creation of a meta-cluster for nodes grouped by brand, allowing for more in-depth analysis of user behavior and preferences.
Figure 4. Hypergraph build from 'brand' attribute among item co-attributes
Let's examine meta clusters formed by users with similar brand preferences. By analyzing these clusters, we can identify trends and develop strategies for brand-specific marketing and product planning.
If this analysis were performed using only a property graph, it would provide insights into the relationship between users and items. However, a hypergraph allows for a more detailed analysis through hyperedges, enabling a deeper exploration of the item's properties (e.g., brand).
Hypergraphs Enhance Visibility of Complex Relationships
Understanding the benefits of hypergraphs involves recognizing multiple common attributes and relationships. How would converting a complex property graph with numerous properties and relationships into a hypergraph change the analysis?
If the user-item node interaction expresses a single kind of relationship, the phenomenon can be analyzed clearly and concisely, as shown in Figure 3. However, if you want to examine various relationships and characteristics of users and items, the analysis becomes more complex. This complexity arises because you must first recognize the interaction between the user and the item, and then analyze them through multiple nodes and relationships by looking for additional characteristics.
By using a hypergraph, you can simplify this complexity, making it easier to visualize and analyze the intricate web of relationships and characteristics.
Figure 5. Property Graph
Figure 6. Graph converted into a Hypergraph
The best way to approach this analysis is by applying a hypergraph. The image above illustrates the transition from a property graph to a hypergraph. Unlike traditional property graphs, a hyper-edge represents the behaviors of users A and C, who have left the same review on a common item. This approach allows us to discover relationship-based meta-users that would have been difficult to observe in existing property graphs.
This meta-data management helps to add and remove user and item characteristics that meta-users possess, transforming them into hyper-edges and processing meta-to-purpose. Furthermore, hypergraphs can visualize multiple overlapping hyper-edges in layers. The resulting visualizations make it easier to analyze complex relationships and behaviors within the data.
Figure 7. Multi-layer graph
Figure 7 represents the hyper-edges in Figure 6 divided into multiple layers. Nodes represent users as before, and the added layers show common relationships or shared characteristics. This process helps analyze the phenomenon layer by layer and uncover new insights. For example, among customers A, B, and C of similar ages, B and C can link up their purchases of similarly priced items. You can also analyze customers A and B, who purchased the same category from different brands.
Using each layer to derive insights offers a multi-layer advantage. You can link all four layers—age, price, brand, and category—to see the chain reaction to the phenomenon at a glance.
As discussed in this article, it is recommended to select and/or combine the use of property and hypergraphs depending on the relational nature of your data.
A hybrid graph allows one to analyze phenomena concisely based on relationships and characteristics between individuals or groups. The hybrid graph encourages collaboration by visualizing abstract causal relationships in detail. Furthermore, it enables the analysis of chains through hyper-edge division into layers.
Deriving insights from relationships and linking them to business in a modern society where connections between people or objects are fused can be challenging. From SNS to blockchain and IoT, the layers of relationships and networks are used in many situations today, making it difficult to fully utilize obtained data.
Bitnine Global is your solution for all data analysis, management, and visualization.
Interested in learning more about bringing your data into hybrid graphs?
Contact us today to get in touch with our graph experts and learn more!