Adchemy, Inc.

David Robert Kauchak

Monifa Akanke Porter

Shreehari Manikarnika

Richard Edward Chatwin

In one embodiment, a method includes accessing online advertising data including metric values for one or more online advertising spaces; identifying pairs of the user variables; for each of the pairs, calculating a statistical score for each of the online advertising metrics and calculating a weighted score based on all the statistical scores for the pair; selecting one of the pairs based on its weighted score; and performing a merge test on the selected pair; and merging the user variables in the selected one of the pairs with each other if the selected one of the pairs passes the merge test.

Grouping user features based on performance measures

In one embodiment, a method includes accessing metric values for one or more online advertising spaces for placing one or more online advertisements; generating a first user variable subgroup and a second user variable subgroup from a user variable group; deriving first metric values for the first user variable subgroup; deriving second metric values for the second user variable subgroup; for each of the online advertising metrics, using a particular statistical model to calculate a statistical score based on the first and second metric values with respect to the online advertising metric and the first and second user variable subgroups; performing a split test on the first user variable subgroup and the second user variable subgroup; and splitting the user variable group into two separate user variable groups if the first and second variable subgroups pass the split test.

In particular embodiments, annotating each keyword of a plurality of keywords with one or more labels of a plurality of labels, comprising: for each label, computing a score for the keyword document corresponding to the keyword and the label using an annotation model; and annotating the keyword with a specific label where the keyword document corresponding to the keyword and the specific label have the highest or the lowest score. Constructing a classifier based on a plurality of training keywords. For each keyword of the plurality of keywords, for each label annotating the keyword, calculating a second index-wise product between a word count vector of the keyword document corresponding to the keyword and a word count vector of the label document corresponding to the label; and predicting whether the label annotating the keyword is correct using the classifier with the second index-wise product as an input to the classifier.

Generating targeted paid search campaigns

In particular embodiments, constructing a classifier based on a plurality of training keywords. Each training keyword comprises one or more words and has a corresponding training keyword document. Constructing the classifier comprises: for each training keyword of the plurality of training keywords, annotating the training keyword with one or more labels of a plurality of labels, each label having a corresponding label document; and for each label annotating the training keyword, determining a correctness of the label; calculating a first index-wise product between a word count vector of the training keyword document corresponding to the training keyword and a word count vector of the label document corresponding to the label; and forming a pair of the correctness and the first index-wise product; and training the classifier using one or more pairs of the correctness and the first index-wise product.

In particular embodiments, advertising groups may be formed based on keywords. Access a plurality of keywords, each keyword comprising one or more words and describing one or more characteristics or features of particular advertising subject matter. Annotate each of the keywords with one or more labels selected from a plurality of labels, each label comprising one or more words and describing one or more aspects of or one or more categories or concepts represented by the keyword. Group the keywords into one or more keyword groups based on similarities between the labels of the keywords, each keyword group comprising one or more of the keywords. Form one or more advertising groups from the keyword groups, comprising from each of the keyword groups, form one or more of the advertising groups, each of the advertising groups comprising one or more of the keywords in the keyword group.

Growing community of inventors

San Mateo, CA, United States of America

David Robert Kauchak