| CPC G06F 16/9535 (2019.01) [G06F 16/9536 (2019.01); G06F 16/9538 (2019.01)] | 20 Claims |
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1. A computer implemented method comprising:
obtaining a set of search queries that have been issued by a plurality of user devices;
determining, for each instance of each query in the set of search queries, contextual data representing a context in which the query was issued and user interactions with search results pages provided in response to the query;
inputting, to a learning model and for a first query in the set of search queries, the determined contextual data for each instance in which the first query was issued, wherein the learning model (1) outputs a likelihood that a search query will be issued in the future and (2) is trained using contextual data determined for a set of training queries and a corresponding set of labels for the set of training queries, wherein each label indicates whether a training query has been issued a threshold number of times;
obtaining, from the learning model and based on the input contextual data, a likelihood that the first query will be issued in the future;
identifying the first query as a repeatable query based on the likelihood that the first query will be issued in the future satisfying a repeatability threshold;
storing the first query with other repeatable search queries that have been previously identified as repeatable queries;
providing, on a user device, a user selectable interface component that, upon being selected by a user device and without receiving a user input of a component of a query, results in issuance of a query from among the repeatable queries;
receiving, from the user device, a first selection of the user selectable interface component that requests issuance of a particular query from among the repeatable queries; and
providing, by a search engine and in response to receiving the first selection from the user device, a first search results page including search results for the particular query.
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