Interactive cluster-based personalized retrieval on large document collections

Abstract

Lately, many systems and websites add personalization functionalities among their provided services. However, for large document collections it is difficult for the user to direct effective queries from the beginning of his/her search, since accurate query terms may not be known in advance. In this paper we describe a system that applies a hybrid approach to assist a user identify the most relevant documents: at the beginning it applies dynamic personalization techniques based on user modeling to initiate the search on a large document and multimedia content collection; next the query is further refined using a clustering based approach which after processing a sub-collection of documents presents the user with more categories to select from a list of new keywords. We analyze the most prominent implementation choices for the modular components of the proposed architecture: a machine learning approach for personalized services, a clustering based approach towards a user directed query refinement and a parallel processing module that supports document clustering in order to decrease the system's response times. © 2008 Springer-Verlag Berlin Heidelberg.