The true lift model: a novel data mining approach to response modeling in database marketing
ACM SIGKDD Explorations Newsletter
Large linear classification when data cannot fit in memory
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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A long-standing goal in advertising is to reduce wasted costs due to advertising to people who are unlikely to buy, as well as to those who would make a purchase whether they saw an ad or not. The ideal audience for the advertiser are those incremental users who would buy if shown an ad, and would not buy, if not shown the ad. On the other hand, for publishers who are paid when the user clicks or buys, revenue may be maximized by showing ads to those users who are most likely to click or purchase. We show analytically and empirically that an optimization towards one metric might result in an inferior performance in the other one. We present a novel algorithm, called SLC, that performs a joint optimization towards both advertisers' and publishers' goals and provides superior results in both.