Applying Diversity and Novelty to Personalized Search
Abstract
Recent research into retrieving and reordering search results so that they cover a maximum number of topics and information needs has gained traction as a means to assist navigation through the explosion of available online information. This problem is also addressed by personalized search: the retrieving and reordering of search results biased to the preferences of a particular user. We investigate a search personalization system that builds a user model from the latent topics mined from their queries and clicked documents. We find that a user’s clicked documents exist in a specific area of the latent topic space. By “diversifying” search results biased to our user model we can reorder search results to the user’s preferences.
Scatter plot of the location of each query and document the user clicked on for that query in topic space. The user appears to occupy a distinguished manifold in 3-topic space. All code written in R and Ruby. Download the personalized search topics paper.