As e-commerce has evolved into its second generation, where the available products are becoming more complex and their abundance is almost unlimited, the task of locating a desired choice has become too difficult for the average user. Therefore, more effort has been made in recent years to develop recommender systems that recommend products or services to users so as to assist in their decision-making process. In this article, we describe crucial experimental results about a novel recommender technology, called the preference-based organization (Pref-ORG), which generates critique suggestions in addition to recommendations according to users' preferences. The critique is a form of feedback ("I would like something cheaper than this one") that users can provide to the currently displayed product, with which the system may better predict what the user truly wants. We compare the preference-based organization technique with related approaches, including the ones that also produce critique candidates, but without the consideration of user preferences. A simulation setup is first presented, that identified Pref-ORG's significantly higher algorithm accuracy in predicting critiques and choices that users should intend to make, followed by a real-user evaluation which practically verified its significant impact on saving users' decision effort.