Hierarchical generation of booking probability

A computing system comprising:

• a memory that stores instructions;
• and
• one or more processors configured by the instructions to perform operations comprising:
• generating, from feature values extracted from historical marketplace data associated with an online marketplace, sets of training clusters, each training cluster including feature values describing similar listings posted to the online marketplace;
• training a first machine learning model based on the set of training clusters, yielding a first trained machine learning model for generating cluster-level probability values that listings will be booked on specified booking dates;
• training a second machine learning model based on an output of training the first trained machine learning model, yielding a second trained machine learning model for generating listing-level probability values that listings will be booked on specified booking dates;
• receiving a request for a probability that a listing will be booked on a given date;
• extracting a set of feature values describing the listing, the set of feature values corresponding to a plurality of features and having been generated from first listing data describing the listing, the set of feature values extracted from a datastore comprising sets of feature values describing a plurality of listings;
• determining a cluster of similar listings to the listing, the cluster of similar listings being determined based on a comparison of the set of feature values describing the listing and the sets of feature values describing the plurality of listings;
• generating a set of cluster feature values for the cluster of similar listings based on the set of feature values describing the listing and sets of feature values describing the cluster of similar listings;
• using the set of cluster feature values and a booking price as input into the first trained machine learning model, to generate a cluster-level probability value indicating a first likelihood that the listing will be booked on the given date;
• using the set of feature values describing the listing and the booking price into the second trained machine learning model, to generate a listing-level probability value indicating a second likelihood that the listing will be booked on the given date;
• and
• generating a final probability value indicating a third likelihood that the listing will be booked on the given date by combining the cluster-level probability value and the listing-level probability value.

A non-transitory computer-readable medium comprising instructions stored thereon that are executable by at least one processor to cause a computing device associated with a first data owner to perform operations comprising:

• generating, from feature values extracted from historical marketplace data associated with an online marketplace, sets of training clusters, each training cluster including feature values describing similar listings posted to the online marketplace;
• training a first machine learning model based on the set of training clusters, yielding a first trained machine learning model for generating cluster-level probability values that listings will be booked on specified booking dates;
• training a second machine learning model based on an output of training the first trained machine learning model, yielding a second trained machine learning model for generating listing-level probability values that listings will be booked on specified booking dates;
• receiving a request for a probability that a listing will be booked on a given date;
• extracting a set of feature values describing the listing, the set of feature values corresponding to a plurality of features and having been generated from first listing data describing the listing, the set of feature values extracted from a datastore comprising sets of feature values describing a plurality of listings;
• determining a cluster of similar listings to the listing, the cluster of similar listings being determined based on a comparison of the set of feature values describing the listing and the sets of feature values describing the plurality of listings;
• generating a set of cluster feature values for the cluster of similar listings based on the set of feature values describing the listing and sets of feature values describing the cluster of similar listings;
• using the set of cluster feature values and a booking price as input into the first trained machine learning model, to generate a cluster-level probability value indicating a first likelihood that the listing will be booked on the given date;
• using the set of feature values describing the listing and the booking price into the second trained machine learning model, to generate a listing-level probability value indicating a second likelihood that the listing will be booked on the given date;
• and
• generating a final probability value indicating a third likelihood that the listing will be booked on the given date by combining the cluster-level probability value and the listing-level probability value.