Abstract: The technology disclosed relates to deduplicating metadata about places. A feature generator module is configured to generate features for metadata profiles. The metadata profiles represent a plurality of places. The features are based on geohash strings and word embeddings generated for the metadata profiles. A diff generator module is configured to generate diff vectors that pair-wise encode results of comparison between features of paired metadata profiles. A classification module is configured to generate similarity scores for the paired metadata profiles based on the diff vectors. A particular similarity score indicates whether metadata profiles in a particular pair of metadata profiles represent a same place.

Abstract: The technology disclosed relates to deduplicating metadata about places. A feature generator module is configured to generate features for metadata profiles. The metadata profiles represent a plurality of places. The features are based on geohash strings and word embeddings generated for the metadata profiles. A diff generator module is configured to generate diff vectors that pair-wise encode results of comparison between features of paired metadata profiles. A classification module is configured to generate similarity scores for the paired metadata profiles based on the diff vectors. A particular similarity score indicates whether metadata profiles in a particular pair of metadata profiles represent a same place.

Abstract: The technology disclosed uses machine learning-based models to compare and merge metadata profiles from various metadata sources to create a library of metadata profiles with the most accurate attributes. The technology disclosed adjusts the geospatial coordinates of places located inside a building structure for accuracy and defines a block for each place within the perimeter of the building structure. The building structure may host a single place or multiple places. The non-overlapping blocks, the adjusted geospatial coordinates of places and more accurate metadata profiles enable the gathering of accurate location-specific data from visitors inside the building structures through their personal devices.

Abstract: The technology disclosed uses machine learning-based models to compare and merge metadata profiles from various metadata sources to create a library of metadata profiles with the most accurate attributes. The technology disclosed adjusts the geospatial coordinates of places located inside a building structure for accuracy and defines a block for each place within the perimeter of the building structure. The building structure may host a single place or multiple places. The non-overlapping blocks, the adjusted geospatial coordinates of places and more accurate metadata profiles enable the gathering of accurate location-specific data from visitors inside the building structures through their personal devices.