Abstract

Search engine logs contain a large amount of click-through data that can be leveraged as soft indicators of relevance. In this paper we address the sponsored search retrieval problem which is to find and rank relevant ads to a search query. We propose a new technique to determine the relevance of an ad document for a search query using click-through data. The method builds on a collaborative filtering approach to discover new ads related to a query using a click graph. It is implemented on a graph with several million edges and scales to larger sizes easily. The proposed method is compared to three different baselines that are state-of-the-art for a commercial search engine. Evaluations on editorial data as well as online traffic data indicate that the model discovers many new ads not retrieved by the baseline methods. The ads from the new approach are on average of better quality than the baselines. In addition to the proposed approach our experimental methodology of evaluation on live traffic is a novel contribution to the academic literature.

Citations