What is a hybrid recommendation model in real estate?

A hybrid recommendation model in real estate combines two or more recommendation approaches most commonly collaborative filtering and content-based filtering to produce more accurate, diverse, and personalised property suggestions. By merging the strengths of different techniques, hybrid models overcome the individual limitations of each approach.

Why Hybrid Models Are Needed

• Collaborative filtering struggles with new users and new listings (cold-start problem).
• Content-based filtering can create a 'filter bubble' by only recommending similar properties.
• Hybrid models use content-based signals for new users and collaborative signals as behaviour data grows.
• This results in recommendations that are both personalised and diverse.

Types of Hybrid Approaches

• Weighted hybrid: Combines scores from multiple models with defined weights.
• Switching hybrid: Uses collaborative filtering when data is sufficient; switches to content-based for new users.
• Stacking: Output of one model feeds into another as additional features.
• Feature augmentation: Collaborative signals are added as features to a content-based model.

Hybrid recommendation models represent the state of the art in PropTech personalisation. Platforms that invest in building robust hybrid systems deliver superior discovery experiences, which translates directly into higher user engagement, retention, and conversion.