How can a knowledge graph be used in recommendation systems?

A knowledge graph enhances recommendation systems by modeling relationships between users, items, and contextual data. Unlike traditional collaborative filtering, which relies on user-item interactions alone, a knowledge graph connects entities (e.g., movies, genres, actors) and their attributes in a structured network. For example, a movie recommendation system could link users to movies they’ve watched, movies to genres or directors, and actors to other movies they’ve starred in. This structure allows the system to traverse connections beyond direct interactions, such as recommending a film because it shares a director with a movie the user liked, even if the genre differs. By embedding these relationships, the system can generate recommendations that account for nuanced patterns and contextual factors.

Knowledge graphs also address cold-start problems, where new users or items lack sufficient interaction data. For instance, a new movie added to the graph can immediately be linked to existing entities like its genre, director, or actors. If a user has previously enjoyed other films by that director, the system can recommend the new movie even without user ratings. Similarly, a new user who specifies an interest in “science fiction” can receive recommendations based on the genre’s connections to popular movies, bypassing the need for explicit interaction history. This approach reduces dependency on sparse data and leverages the graph’s semantic relationships to infer relevance.

Finally, knowledge graphs improve recommendation diversity and explainability. Traditional methods often suggest items similar to what a user already likes, creating a “filter bubble.” A knowledge graph can instead recommend items connected through varied paths, such as a book adaptation of a movie the user enjoyed or a documentary about an actor in their favorite film. Additionally, the graph’s structure makes it easier to explain recommendations by tracing connections (e.g., “Recommended because you liked Actor X in Movie Y, and they also star in Movie Z”). This transparency builds user trust and provides actionable insights for developers to refine the model. By combining rich contextual data with explicit relationships, knowledge graphs enable more flexible, accurate, and interpretable recommendations.