A Luhn-Inspired Vector Re-weighting Approach for Improving Personalized Web Search

A fundamental problem with current Web search technology is that in the absence of any additional information, the same query provided by two different searchers will produce the same set of search results, even if the information needs of the searchers are different. Web search personalization has been proposed as a solution to this problem, whereby the interests and preferences of individual users are modelled and used to affect the outcomes of their subsequent searches. A common approach is to generate vector-based models of searchers' interests, and re-rank the search results based on the similarity of the documents to these models. In this paper, a novel approach is proposed to automatically identify and re-weight significant dimensions in vector-based models in order to improve the personalized order of Web search results. This approach is inspired by Luhn's model of term importance, which is rooted in Zipf's Laws. Evaluations with a set of ambiguous queries illustrate the effectiveness of this approach.