BxD Primer Series: Knowledge-based Recommendation Models

Hey there ??

Welcome to BxD Primer Series where we are covering topics such as Machine learning models, Neural Nets, GPT, Ensemble models, Hyper-automation in ‘one-post-one-topic’ format. Today’s post is on?Knowledge-based Recommendation Models. Let’s get started:

The What:

Knowledge-based recommendation models utilize explicit knowledge about the user's preferences and the characteristics of the items being recommended to generate personalized recommendations. In contrast to other recommendation systems, such as collaborative filtering or content-based systems, knowledge-based models rely on?explicit information about the user and the items?to make recommendations.

The basic process of a knowledge-based recommendation model involves collecting information about the user's preferences and the attributes of the items being recommended. This information is typically represented in a knowledge base, which can take the form of a database, ontology, or other structured representation of knowledge. The model then uses this information to make recommendations by matching user's preferences with attributes of items being recommended.

Although any structured knowledge database can be used to make recommendations using similar technique as content-based filtering recommendation model (check?here), we will primarily focus on knowledge graphs, which is a new concept in our series.

Introduction to Knowledge Graphs:

In a knowledge graph, data is represented as nodes (entities) and edges (relationships) between these entities. Each entity is assigned a unique identifier, and each relationship is defined by a predicate, which specifies the type of relationship between the entities.

A knowledge graph can be used to represent domain knowledge of items and their relationships. For example, in an e-commerce setting, knowledge graph can represent the relationships between products, such as "is similar to", "is a substitute for", and "is frequently bought together with" etc.

It can represent relationship between users and items. For example, “liked”, “clicked”, “added to wishlist”, “bought”, “pending in cart” etc.

It can represent relationship between items and features. For example, “black”, “26 inch waist”, “on discount”, “authored by”, “handwritten” etc.

Similarly, user-features, user-user and other relationship graphs can be created.

The How:

Knowledge-based Recommendation Models use all available explicit information about items and users to generate recommendations. This process has four general steps:

1. Represent data as a knowledge graph:?This involves identifying entities and relationships between them, and creating a graph structure that captures this information.
2. Understand user preferences:?This can be done by analyzing their past behavior, such as their search history, purchase history, or interactions with platform.
3. Identify relevant items:?Using the knowledge graph, identify items that are relevant to user based on their preferences. For example, if the user has shown a preference for certain types of products or content, you can identify other items that are related to those preferences.
4. Rank the items:?Once you've identified relevant items, you can use various algorithms to rank them based on their relevance to the user. This can be done using techniques such as collaborative filtering, content-based filtering, or hybrid approaches that combine both.

Represent Data as a Knowledge Graph:

1. Identify entities: The first step is to identify the entities within your data. An entity is anything that can be uniquely identified, such as a person, product, or event.
2. Model relationships:?Define the types of relationships that exist between your entities. For example, define a "purchased" relationship between a customer and a product, with attributes such as the date of purchase or the quantity purchased. You can also define hierarchies or taxonomies of relationships, such as subcategories within a category. For example if purchased in last 7 days: “purchased recently” or “purchased as gift” etc.
3. Create the graph:?This involves creating nodes for each entity and attributes, and edges for each relationship. You can use graph databases such as Neo4j, RDF stores such as Virtuoso or Stardog, or graph processing frameworks such as Apache Spark or Apache Flink.

The Why:

Reasons to use knowledge-based recommendation models:

1. Explicit knowledge representation:?Knowledge graphs can represent the explicit knowledge of items and their features, which can lead to more accurate recommendations based on the user's preferences and interests.
2. Interoperability:?Knowledge graphs can integrate data from various sources and formats, making it possible to provide recommendations across different domains and applications.
3. Flexibility:?Knowledge graphs can be easily updated and modified, allowing the recommendation model to adapt to changes in the environment.
4. Domain-specific knowledge:?Knowledge graphs can incorporate domain-specific knowledge, such as ontologies and taxonomies, to improve the quality of recommendations in specialized domains.

The Why-Not:

Reasons to not use knowledge-based recommendation models:

1. Complexity:?Building and maintaining a knowledge graph is complex and require expertise in data modeling and graph database technology.
2. Data quality:?The quality of recommendations depends on the quality of data used to populate the knowledge graph. Incomplete or inaccurate data negatively impact quality of recommendations.
3. Scalability:?With multiple data streams, knowledge graphs become large and complex, which can impact performance of model if relevant recommendations are not prioritized.
4. Lack of diversity:?Knowledge-based recommendation models using knowledge graphs may be biased towards popular items or items that are similar to items the user has previously interacted with, which leads to a lack of diversity in recommendations.

Time for you to support:

1. Reply to this email with your question
2. Forward/Share to a friend who can benefit from this
3. Chat on Substack with BxD (here)
4. Engage with BxD on LinkedIN (here)

In next coming posts, we will cover two more recommendation models - Matrix Factorization, Hybrid Recommender Systems.

Let us know your feedback!

Until then,

Have a great time! ??

#businessxdata?#bxd?#knowledge?#based?#recommendationsystems?#primer