Recommendations using Knowledge graphs

-This is an introduction on how knowledge graph helps to power more accurate RECOMMENDATIONS in real-time.

Brief Intro:

Knowledge graphs (KGs) organize data from multiple sources, capture information about entities of interest in each domain or task (like people, places or events), and forge connections between them. A knowledge graph formally represents semantics by describing entities and their relationships. Knowledge graphs may make use of ontologies as a schema layer. Doing this allows logical inference for retrieving implicit knowledge rather than only allowing queries requesting explicit knowledge.

KG interest group of folks within the organization who can facilitate research & innovation within critical areas of data science & AI. In data science and AI, knowledge graphs are commonly used to:

a)Facilitate access to and integration of data sources

b)Add context and depth to other, more data-driven AI techniques such as machine learning;

c)Serve as bridges between humans and systems, such as generating human-readable explanations, or, on a bigger scale, enabling intelligent systems for scientists and engineers.

Knowledge representation formalisms; natural language processing; machine learning; methodologies to construct, learn and manage ontologies and knowledge bases; de facto standard ontologies and vocabularies; scalable reasoning, linked data etc..

Figure 1- Example of KG representation in graph

Knowledge Graphs & Semantics:

With the seminal approaches to “semantic computing”, the researchers represent the knowledge using graphs consisting of nodes and edges. Such a representation mechanism has many advantages both in organizing and retrieving knowledge. The entities and concepts are represented as nodes and the relationships connecting these nodes are denoted by edges. For example, consider the sentence “A recommendation system is a subclass of information filtering system that seeks to predict the rating or preference”. In this case, entities such as “recommendation system”, “information filtering system”, “rating”, “preference” will be represented as nodes and possible relationships such as “subclass of”, “predict” will be represented as the edges connecting these entities. The term “knowledge graph” was first introduced by Google in the year 2012 when they started to introduce the notion of semantics into their search algorithms. But now the term is widely used by other knowledgebase providers , consider any knowledge base as a knowledge graph if it exhibits

1) represents entities and their relationships in nodes and edges

(2) the classes and/or concepts and their relationships in a schema or ontology

(3) has wide coverage in various domains

(4) potentially link entities with other knowledge bases or graphs

Knowledge graphs in recommendation system:

Recommendation systems use machine learning algorithms to analyze data and provide personalized recommendations to users. By integrating knowledge graphs into these systems, we can provide more accurate and relevant recommendations based on the relationships between different items. In a recommendation system, we can use a knowledge graph to represent the relationships between items and users, as well as the relationships between different items.

For example, let’s say we have an e-commerce website that sells books. We can use a knowledge graph to represent the relationships between different books based on factors such as genre, author, and subject matter. We can also represent the relationships between users based on their browsing and purchasing history. Using this knowledge graph, we can then use machine learning algorithms to provide personalized recommendations to users. For example, if a user has recently purchased a book on a particular topic, we can use the knowledge graph to recommend other books that are related to that topic.

By integrating knowledge graphs into recommendation systems, we can provide more accurate and relevant recommendations to users. This can help to improve user engagement and satisfaction, which can ultimately lead to increased sales and revenue for businesses.

How Knowledge graphs works in Recommendation Systems:

knowledge graphs can improve recommendation systems by capturing the complex relationships between different items and users. By using a knowledge graph, recommendation systems can provide more accurate and relevant recommendations to users, leading to higher engagement and satisfaction.

1. Data Collection: The first step is to collect data about users and items. This data can include user profiles, item attributes, user-item interactions (such as views, purchases, and ratings), and any other relevant data.
2. Graph Construction: Once the data is collected, a knowledge graph can be constructed that represents the relationships between different items and users. Each item and user is represented as a node in the graph, and the relationships between them are represented as edges.
3. Graph Analysis: Once the graph is constructed, machine learning algorithms can be used to analyze the graph and make recommendations. These algorithms can use a variety of techniques, such as collaborative filtering, content-based filtering, and hybrid filtering.
4. Recommendation Generation: Based on the analysis of the graph, personalized recommendations can be generated for each user. These recommendations can be displayed to the user on the website or app, and can be updated in real-time as the user interacts with the system.

Industry-wise use cases of Recommendations using Knowledge graphs:

1. Oil & Gas / Manufacturing: Use of knowledge graphs in recommendation systems can help Manufacturing/oil & gas companies to improve their operations and increase efficiency, while also reducing downtime and improving safety. Recommendation helps in demand management , predictive maintenance , Safety recommendations, Equipment optimization, resource management & more.
2. Pharma: the use of knowledge graphs in recommendation systems can help the pharmaceutical industry to improve drug discovery, clinical trials, personalized medicine, and adverse event monitoring.
3. Retail and E-commerce: In the retail and e-commerce industry, knowledge graphs can be used to recommend products to customers based on their purchase history, search queries, and browsing behavior. This can help retailers to personalize the customer experience and increase sales.
4. Media and Entertainment: In the media and entertainment industry, knowledge graphs can be used to recommend movies, TV shows, and other content to viewers based on their viewing history and preferences. This can help streaming platforms to increase user engagement and retention.
5. Healthcare: In the healthcare industry, knowledge graphs can be used to recommend treatment plans to patients based on their medical history and symptoms. This can help healthcare providers to improve patient outcomes and reduce healthcare costs.
6. Financial Services: In the financial services industry, knowledge graphs can be used to recommend investment products and services to customers based on their financial history and goals. This can help financial institutions to improve customer satisfaction and retention.
7. Travel and Hospitality: In the travel and hospitality industry, knowledge graphs can be used to recommend travel destinations, accommodations, and activities to customers based on their preferences and past behavior. This can help travel companies to improve customer loyalty and increase sales.

Technology Advantages:

• KG helps to extend your existing environment while protecting your investments further by increasing data.
• Graphs DB will help to provide full value of connected data within the entire organization as primary or secondary
• KG offers to generate real-time automated data alerts & recommendations across data teams.
• Attention to detail of every increased complex data
• Shift from discrete analytics to connected analytics.
• INDEX FREE ADJECENCY reduces time querying, scans, duplicates within seconds to minutes from weeks to months
• Graph platform can manage all huge relationships, patterns, and lookups which is super quick within seconds.
• Graph platform helps in quick decision making, reasoning, inferencing across all teams including R&D, Product, Solution, production, Quality
• KG reduces your hardware & operational costs, with no architecture limitations in scalability along with granular security controls.
• Graphs are the foundation to do 360-degree analytics covering & combining all types of data in silos.

Business benefits:

Companies around the globe have started incorporating Knowledge graphs into their data architecture to take advantage of powerful real-time recommendations. These enterprises have experienced a variety of business benefits :

1. Improved Competitiveness: Graph Technology enables new types of business functionality that are often not possible with other technologies, allowing you to make real-time decisions based on connected data. For example, Walmart uses Neo4j to make real-time product recommendations by using information about what users prefer. Additionally, many dating and online job sites use Neo4j to recommend jobs or dates by incorporating knowledge of the extended network (friends of friends) into the recommendation, again in real time, substantially improving the accuracy of the recommendation.

2) Reduced Project Time & Cost: Graph technology cuts the overhead on many types of projects, particularly those involving connected data. Many customers cite the huge acceleration that occurs when a graph model is brought to bear on a connected data problem. For example, eBay cites that with Neo4j it requires 10–100 times less code than it did with SQL, and Telenor, one of the world’s top telecom providers, uses Neo4j for the authorization system on its business customer portal, improving performance by 1,000 times.

3) Faster Product time to Market & Better performance: Graphs enable developers to produce less code than RDBMS alternatives. Less code means higher quality and an increased success rate on projects. graph database performance is dramatically better for connected datasets — often the difference between what will and won’t be possible for a development team.