Agents are the catalysts for commerce on the Web today. For example, comparison-shopping agents mediate the interactions between consumers and suppliers in order to yield markets that are more efficient. However, today's shopping agents are price-dominated, unreflective of the nature of supplier/consumer differentiation or the changing course of differentiation over time. This paper aims to tackle this dilemma and advances shopping agents into a stage where both kinds of differentiation are taken into account for enhanced understanding of the realities. We call them personalized and integrative shopping agents. These agents can leverage the interactive power of the Web for a more accurate understanding of consumer's preferences. This paper then presents a comparison-shopping engine that can be easily instantiated to become personalized and integrative shopping agents. This engine comprises of a product/merchant information collector, a consumer behavior extractor, a user profile manager, and an on-line learning personalized ranking module. We have built this engine and instantiated a comparison-shopping system for collecting preliminary evaluation results. The results show that this system is quite promising in overcoming the reality challenges of comparison shopping. In order to strengthen the contributions of this engine, we also gave a fielded application of this engine for personalized travel information discovery and explained the great potentials of this engine for a variety of comparison-shopping tasks.