Robert J. Ragno

Predicting clicks: estimating the click-through rate for new ads

Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. This ranking has a strong impact on the revenue the search engine receives from the ads. Further, showing the user an ad that they prefer to click on improves user satisfaction. For these reasons, it is important to be able to accurately estimate the click-through rate of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. We show that we can use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads. We also show that using our model improves the convergence and performance of an advertising system. As a result, our model increases both revenue and user satisfaction.

Inferring similarity between music objects with application to playlist generation

The growing libraries of multimedia objects have increased the need for applications that facilitate search, browsing, discovery, recommendation and playlist construction. Many of these applications in turn require some notion of distance between, or similarity of, such objects. The lack of a reliable proxy for similarity of entities is a serious obstacle in many multimedia applications.In this paper we describe a simple way to automatically infer similarities between objects based on their occurrences in an authored stream. The method works both for audio and video. This allows us to generate playlists by emulating a particular stream or combination of streams, recommend objects that are similar to a chosen seed, and derive measures of similarity between associated entities, such as artists.

K-Best Suffix Arrays

Suppose we have a large dictionary of strings. Each entry starts with a figure of merit (popularity). We wish to find the k-best matches for a substring, s, in a dictinoary, dict. That is, grep s dict | sort -n | head -k, but we would like to do this in sublinear time. Example applications: (1) web queries with popularities, (2) products with prices and (3) ads with click through rates. This paper proposes a novel index, k-best suffix arrays, based on ideas borrowed from suffix arrays and kdtrees. A standard suffix array sorts the suffixes by a single order (lexicographic) whereas k-best suffix arrays are sorted by two orders (lexicographic and popularity). Lookup time is between log N and sqrt N.