conditional distribution of the topics users clicked on k within a collection p k collection as the target distribution statistical learning algorithms such as maximum entropy and support vector machines can be used to train the parameters to improve the estimation of p t collection this requires enough user action data to make p k collection reliable but once that 39 s the case the two distributions will become a positive feedback loop that maximizes the relevancy of the links chosen in the collection that is as more data is collected from what users actually clicked on it will improve the automatic link selection algorithm which in turn will generate more user clicks since they are more relevant independent of the user action data for any collection the dsi system needs to select high quality links so users will click on them to bootstrap the feedback loops this is accomplished by relying on the prior distribution p t which estimates the importance of topic t across all collections for this estimation any large collection can be used including documents on the world wide web arguably today 39 s largest publicly accessible collection the larger the collection the more reliable the estimation of p t becomes the estimation of p t includes a mixture of factors including the number of time it appears within the collection the number of documents it appears in the ratio of documents containing.