| CPC G06F 16/2237 (2019.01) [G06F 16/2438 (2019.01); G06F 16/287 (2019.01); G06F 16/288 (2019.01); G06N 3/045 (2023.01); G06N 5/02 (2013.01)] | 13 Claims |
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1. A method comprising:
selecting, during a training phase associated with a neural network, from one or more search logs, a set of pairs, wherein each pair comprises:
a query; and
a previously answered frequently asked question associated with the query;
generating, by the neural network executed by a processing device, a set of embedding vectors for each pair in the set of pairs, resulting in a plurality of embedding vectors for previous queries, wherein each set of embedding vectors comprises an embedding of the query and an embedding of the previously answered frequently asked question associated with the query;
generating an entity-specific index, wherein the entity-specific index comprises a set of indices for the plurality of embedding vectors for previous queries;
receiving, from an end user system, a search query associated with an entity associated with the entity-specific index;
generating, by the neural network, at least one query embedding based on the search query;
comparing, using the entity-specific index, the at least one query embedding to the plurality of embedding vectors for previous queries using a distance measurement, resulting in a ordered list of frequently asked questions corresponding to the search query based on the distance measurement between the at least one query embedding and the plurality of embedding vectors for previous queries; and
generating a search result in response to the search query, wherein the search result comprises the frequently asked question within the ordered list having a smallest distance.
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