Optimizing search engines using clickthrough data

Abstract

This paper presents an approach to automatically optimiz- ing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous ap- proaches to learning retrieval functions from examples exist, they typically require training data generated from relevance judgments by experts. This makes them difficult and ex- pensive to apply. The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Ma- chine (SVM) approach, this paper presents a method for learning retrieval functions. From a theoretical perspective, this method is shown to be well-founded in a risk minimiza- tion framework. Furthermore, it is shown to be feasible even for large sets of queries and features. The theoreti- cal results are verified in a controlled experiment. It shows that the method can effectively adapt the retrieval function of a meta-search engine to a particular group of users, out- performing Google in terms of retrieval quality after only a couple of hundred training examples. 1.