Personalized property recommender system using artificial reality and machine learning

Abstract

Implementations generate personalized property recommendations based on experiential data obtained via artificial reality systems and machine learning predictions that correlate user preferences to these properties. A user can be one or more persons interested in purchasing a property, such as a new home. A property buying workflow often includes defining a set of preferences for a desired property and comparing properties for sale with these defined preferences. However, in a conventional buying workflow, defining preferences and doing the comparison is left up to the user and/or the user's real estate agent. Users can often feel overwhelmed by the information, in particular users that do not have previous buying experience. Implementations can integrate machine learning and artificial reality systems into the property buying experience to mitigate the information overload problem and provide meaningful recommendations to the user, such as a ranking of properties that comply with user's preferences.

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