| CPC G06F 16/9535 (2019.01) [G06F 16/9536 (2019.01); G06F 16/9538 (2019.01)] | 19 Claims |
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1. A computer-implemented method, the method comprising:
obtaining, from a search engine query log and by a computing system comprising one or more processors, a plurality of training queries that have been issued by different user devices;
for each of the plurality of training queries:
determining and storing in the search engine query log, with a context analyzer and based on a context in which a respective training query was issued and user interactions with search results pages in response to the respective training query, a respective contextual dataset, wherein the user interactions are determined by accessing a click log;
obtaining, by the computing system, training dataset, wherein the training dataset comprises a plurality of contextual datasets, a plurality of labels, and the plurality of training queries, wherein each label indicates whether the respective training query was observed to be repeated or otherwise to be repeatable, wherein each of the plurality of contextual datasets comprise contextual data determined for the respective training query of the plurality of training queries, and wherein each of the plurality of labels indicates whether the respective training query has been issued a threshold number of times, wherein each of the plurality of contextual datasets for the plurality of training queries comprises: a number of times that a particular query has been issued by a particular user device and a number of unique user devices that issued the particular query a threshold number of times;
processing, by the computing system, a contextual dataset of the plurality of contextual datasets with a learning model to determine a likelihood that a first training query associated with the contextual dataset will be issued in the future;
generating and storing in storage, by the computing system, a repeatable query determination as an output for the first training query based on the likelihood that the first query will be issued in the future and a repeatability threshold; and
training, by the computing system, the learning model based on the repeatable query determination for the first training query and an output of a label of the plurality of labels for the first training query.
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