the objective: personalized travel and activity recommendations using AI.

The Users find activities like restaurants, parks, and events, tailored to their preferences and location.

The App will offer features such as group planning, social feeds, and trusted reviews from friends.

Why knowledge graphs?

Knowledge graphs excel at representing relationships between entities, which aligns well with your project's focus on user personas, travel destinations, and personal preferences, as well as for social feeds and all things relational (knowledge graphs are based on relations among the nodes → data points)

Entity Relationships

represent travelers, destinations, activities, and other factors as nodes.

→ Relationships like "is interested in," "visited," or "prefers" could link these nodes.

the system this way can infer to new connections based on known relationships.

Contextual Recommendations

Having incorporated the contextual data (e.g., user preferences, location, travel history), the knowledge graph by understanding broader connections between entities can generate more personalized recommendations .

Semantic layers

• linking places with activities through secondary relationships, is called semantic layering and that ability would help above surface recommendations to influence more on the whole system.

NOTE:

Knowledge graphs can provide a clear explanation of why certain recommendations are made by tracing the relationships between nodes, which is often harder with other methods like deep learning models.

SCALING: survey results + user feedback.

Start small and expand the graph as more data (e.g., survey results or user feedback) is gathered: and that’s an easy scaling and continuous improvement.