Abstract: A computer-implemented technique is described herein for modifying original keyword information to increase the probability that it will match the queries input by users. The technique operates by using a search engine to provide supplemental information that is relevant to the original keyword information. The technique then mines the supplemental information to extract frequently-occurring n-grams. Next, the technique removes n-grams that are considered to represent noise, and then uses a deep-structured machine-learned model to assign score values to the remaining n-grams. Finally, the technique supplements and/or replaces the original keyword information with the highest-scoring n-grams.

Abstract: A computer-implemented technique is described herein for modifying original keyword information to increase the probability that it will match the queries input by users. The technique operates by using a search engine to provide supplemental information that is relevant to the original keyword information. The technique then mines the supplemental information to extract frequently-occurring n-grams. Next, the technique removes n-grams that are considered to represent noise, and then uses a deep-structured machine-learned model to assign score values to the remaining n-grams. Finally, the technique supplements and/or replaces the original keyword information with the highest-scoring n-grams.

Abstract: Multimedia features extracted from display advertisements may be integrated into a click prediction model for improving click prediction accuracy. Multimedia features may help capture the attractiveness of ads with similar contents or aesthetics. Numerous multimedia features (in addition to user, advertiser and publisher features) may be utilized for the purposes of improving click prediction in ads with limited or no history.