RECOMMENDATIONS BASED ON BRANDING

1 . (canceled)

2 . A method, comprising:

identifying, using a database of user queries, a plurality of brands included in the user queries;

for ones of the plurality of brands, identifying one or more non-brand terms associated with respective ones of the brands;

determining, for pairs of brands of the plurality of brands, respective recommendation scores that are based on the associated non-brand terms for corresponding pairs of brands, wherein a given recommendation score is indicative of a strength of relationship between a respective pair of brands;

in response to an indication of a user activity performed on a computing device by a user, identifying a preferred brand for the user; and

selecting, based on the respective recommendation scores, one or more different brands as a recommendation to the user.

3 . The method of claim 2 , wherein determining the respective recommendation scores includes identifying, for a particular pair of brands, a common set of non-brand terms from the non-brand terms associated with each of the particular pair of brands.

4 . The method of claim 2 , further comprising, for a particular brand of the plurality of brands, generating an association score for each non-brand term associated with the particular brand, wherein a given association score indicates a strength of association between the particular brand and a respective one of the associated non-brand terms.

5 . The method of claim 4 , further comprising generating, for the particular brand, a vector representation using the association scores.

6 . The method of claim 5 , wherein determining the respective recommendation score for the particular brand and a different brand includes calculating the respective recommendation score using the vector representation of the particular brand and a vector representation of the different brand.

7 . The method of claim 4 , further comprising adding the particular brand to one or more brand categories using the association scores for the non-brand term associated with the particular brand.

8 . The method of claim 2 , further comprising:

identifying a group of queries that include a particular brand;

tokenizing the group of queries into respective sets of terms based on statistically significant phrases included in the group of queries; and

adding the particular brand to one or more brand categories based on the sets of terms.

9 . The method of claim 2 , further comprising:

identifying a set of queries issued by a particular user during a given session on a particular computing device; and

identifying a connection between two brands included in the set of queries.

10 . The method of claim 9 , further comprising determining a strength of connection between the two brands based on identified connections between two brands across a plurality of user sessions.

11 . A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:

identifying a plurality of brands included in one or more of a plurality of user queries that are stored in a database;

identifying one or more non-brand terms associated with respective ones of the plurality of brands;

for pairs of brands of the plurality of brands, determining respective recommendation scores that are based on the associated non-brand terms for corresponding pairs of brands, wherein a given recommendation score is indicative of a strength of relationship between a respective pair of brands;

identifying a preferred brand for a user that is currently active on a computing device; and

selecting, based on the preferred brand and respective recommendation scores, one or more different brands as a recommendation to the user.

12 . The non-transitory machine-readable medium of claim 11 , wherein the operations further comprise:

for a particular brand of the plurality of brands, generating an association score for each non-brand term associated with the particular brand, wherein a given association score indicates a strength of association between the particular brand and a respective one of the associated non-brand terms; and

generating, for the particular brand, a vector representation using the association scores.

13 . The non-transitory machine-readable medium of claim 12 , wherein determining the respective recommendation score for the particular brand and a different brand includes calculating the respective recommendation score using the vector representation of the particular brand and a vector representation of the different brand.

14 . The non-transitory machine-readable medium of claim 12 , wherein the operations further comprise adding the particular brand to one or more brand categories using the association scores for the non-brand term associated with the particular brand.

15 . The non-transitory machine-readable medium of claim 11 , wherein the operations further comprise:

identifying a group of queries including a particular brand;

tokenizing the group of queries into respective sets of terms based on statistically significant phrases included in the group of queries; and

adding the particular brand to one or more brand categories based on the sets of terms.

16 . The non-transitory machine-readable medium of claim 11 , wherein the operations further comprise:

identifying a set of queries issued by a particular user during a given session on a particular computing device;

identifying a connection between two brands included in the set of queries; and

determining a strength of connection between the two brands based on identified connections between two brands across a plurality of user sessions.

17 . A system comprising:

a database having stored therein a plurality of user queries;

a non-transitory memory; and

one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:

identifying, from the database, a plurality of brands included in one or more of the plurality of user queries;

for respective ones of the plurality of brands, associating one or more non-brand terms included in the plurality of user queries;

for pairs of brands of the plurality of brands, determining respective recommendation scores based on the associated non-brand terms, wherein a given recommendation score is indicative of a strength of relationship between a respective pair of brands;

determining a preferred brand for a user that is currently active on a computing device that is coupled, via a network, to the system; and

based on the preferred brand and the respective recommendation scores, identifying one or more different brands for use as a recommendation to the user.

18 . The system of claim 17 , wherein determining the respective recommendation scores includes identifying, for a particular pair of brands, a common set of non-brand terms from the non-brand terms associated with each of the particular pair of brands.

19 . The system of claim 17 , wherein the operations further comprise generating, for a particular brand of the plurality of brands, an association score for each non-brand term associated with the particular brand, wherein a given association score indicates a strength of association between the particular brand and a respective one of the associated non-brand terms.

20 . The system of claim 17 , wherein the operations further comprise:

for a group of queries that include a particular brand, tokenizing ones of the group into respective sets of terms based on statistically significant phrases included in the group of queries; and

adding the particular brand to one or more brand categories based on the sets of terms.

21 . The system of claim 17 , wherein the operations further comprise identifying, for a set of queries issued by a particular user during a given session on a particular computing device, a connection between two brands included in the set of queries.