Adaptive clustering of search results

Clustering of search results has been shown to be advantageous over the simple list presentation of search results. However, in most clustering interfaces, the clusters are not adaptive to a user's interaction with the clustering results, and the important question "how to optimize the benefit of a clustering interface for a user" has not been well addressed in the previous work. In this paper, we study how to exploit a user's clickthrough information to adaptively reorganize the clustering results and help a user find the relevant information more quickly. We propose four strategies for adapting clustering results based on user actions. We propose a general method to simulate different kinds of users and linearize the cluster results so that we can compute regular retrieval measures. The simulation experiments show that the adaptation strategies have different performance for different types of users; in particular, they are effective for "smart users" who can correctly recognize the best clusters, but not effective for "dummy users" who follow system's ranking of results. We further conduct a user study on one of the four adaptive clustering strategies to see if an adaptive clustering system using such a strategy can bring users better search experience than a static clustering system. The results show that there is generally no significant difference between the two systems from a user's perspective.