US 12,730,966 B2Granted

Machine learning techniques for predicting and ranking typeahead query suggestion keywords based on user click feedback

AssigneeOptum, Inc.

InventorsChenwei Liu, Xianshi Wei, Ayush Tomar, Vinit Garg

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Abstract

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for providing suggestion keywords based on historical search data of a user by: generating one or more keyword feature vectors associated with a plurality of keywords from a list of suggestion keywords, generating one or more personalized feature vectors associated with the user based on search session data, generating a plurality of predictions of the user selecting the plurality of keywords based on the one or more keyword feature vectors and the one or more personalized feature vectors, assigning a plurality of rankings to the plurality of keywords based on the plurality of prediction probabilities, and generating one or more typeahead suggestion keywords based on the plurality of rankings.

Claims18

Dependent claims

A computer-implemented method comprising:

receiving, by one or more processors and via a graphical user interface, a word prefix from a computing device of a user;
providing, by the one or more processors and to a personalized re-ranking machine learning model, a plurality of keywords to produce a plurality of predictions on the plurality of keywords, wherein:
(i) the plurality of keywords comprises one or more suggestion keywords that are associated with the word prefix,
(ii) the plurality of predictions on the plurality of keywords is generated by the personalized re-ranking machine learning model based on a plurality of keyword feature vectors and one or more personalized feature vectors,
(iii) the plurality of keyword feature vectors corresponds to the plurality of keywords,
(iv) the one or more personalized feature vectors correspond to search session data that is associated with the user,
(v) the personalized re-ranking machine learning model is stored in association with one or more inferred relevant keywords,
(vi) the one or more inferred relevant keywords are determined by one or more of:
(a) matching one or more search word prefixes with one or more beginning leading characters of one or more terms searched during a same search session associated with the search session data,
(b) matching the one or more search word prefixes with one or more leading characters of one or more middle words of the one or more terms searched, or
(c) matching the one or more search word prefixes with one or more beginning leading characters of one or more equivalent keywords with respect to the one or more terms searched, and
(vii) a plurality of rankings is assigned to the plurality of keywords based on the plurality of predictions;
and
providing, by the one or more processors and via the graphical user interface, the one or more suggestion keywords for the word prefix, responsive to the word prefix received from the computing device, based on the plurality of rankings.

11 more elements

A system comprising

one or more processors and
one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving, via a graphical user interface, a word prefix from a computing device of a user;
providing, to a personalized re-ranking machine learning model, a plurality of keywords to produce a plurality of predictions on the plurality of keywords, wherein:
(i) the plurality of keywords comprises one or more suggestion keywords that are associated with the word prefix,
(ii) the plurality of predictions on the plurality of keywords is generated by the personalized re-ranking machine learning model based on a plurality of keyword feature vectors and one or more personalized feature vectors,
(iii) the plurality of keyword feature vectors corresponds to the plurality of keywords,
(iv) the one or more personalized feature vectors correspond to search session data that is associated with the user,
(v) the personalized re-ranking machine learning model is stored in association with one or more inferred relevant keywords,
(vi) the one or more inferred relevant keywords are determined by one or more of:
(a) matching one or more search word prefixes with one or more beginning leading characters of one or more terms searched during a same search session associated with the search session data,
(b) matching the one or more search word prefixes with one or more leading characters of one or more middle words of the one or more terms searched, or
(c) matching the one or more search word prefixes with one or more beginning leading characters of one or more equivalent keywords with respect to the one or more terms searched, and
(vii) a plurality of rankings is assigned to the plurality of keywords based on the plurality of predictions;
and
providing, via the graphical user interface, the one or more suggestion keywords for the word prefix, responsive to the word prefix received from the computing device, based on the plurality of rankings.

13 more elements

One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

receiving, via a graphical user interface, a word prefix from a computing device of a user;
providing, to a personalized re-ranking machine learning model, a plurality of keywords to produce a plurality of predictions on the plurality of keywords, wherein:
(i) the plurality of keywords comprises one or more suggestion keywords that are associated with the word prefix,
(ii) the plurality of predictions on the plurality of keywords is generated by the personalized re-ranking machine learning model based on a plurality of keyword feature vectors and one or more personalized feature vectors,
(iii) the plurality of keyword feature vectors corresponds to the plurality of keywords,
(iv) the one or more personalized feature vectors correspond to search session data that is associated with the user,
(v) the personalized re-ranking machine learning model is stored in association with one or more inferred relevant keywords,
(vi) the one or more inferred relevant keywords are determined by one or more of:
(a) matching one or more search word prefixes with one or more beginning leading characters of one or more terms searched during a same search session associated with the search session data,
(b) matching the one or more search word prefixes with one or more leading characters of one or more middle words of the one or more terms searched, or
(c) matching the one or more search word prefixes with one or more beginning leading characters of one or more equivalent keywords with respect to the one or more terms searched, and
(vii) a plurality of rankings is assigned to the plurality of keywords based on the plurality of predictions;
and
providing, via the graphical user interface, the one or more suggestion keywords for the word prefix, responsive to the word prefix received from the computing device, based on the plurality of rankings.

11 more elements

Dependencies are inferred from the claim text.