This item is a Poster.
- Chang, William - University of Southern California
- Pantel, Patrick - Yahoo! Laboratories
- Popescu, Ana-Maria - Yahoo! Laboratories
- Gabrilovich, Evgeniy - Yahoo! Laboratories
In online advertising, pervasive in commercial search engines, advertisers typically bid on few terms, and the scarcity of data makes ad matching difficult. Suggesting additional bidterms can signiﬁcantly improve ad clickability and conversion rates. In this paper, we present a large-scale bidterm suggestion system that models an advertiser’s intent and ﬁnds new bidterms consistent with that intent. Preliminary experiments show that our system signiﬁcantly increases the coverage of a state of the art production system used at Yahoo while maintaining comparable precision.
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