Recommender systems combine user profiling and filtering techniques to provide more pro-active and personal information retrieval systems, and have been gaining in popularity as a way of overcoming the ubiquitous information overload problem. Many recommender systems operate as interactive systems that seek feedback from the end-user as part of the recommendation process to revise the user's query. In this paper we examine different forms of feedback that have been used in the past and focus on a low-cost preference-based feedback model, which to date has been very much under utilised. In particular we describe and evaluate a novel comparison-based recommendation framework which is designed to utilise preference-based feedback. Specifically, we present results that highlight the benefits of a number of new query revision strategies and evidence to suggest that the popular more-like-this strategy may be flawed.
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