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The increasing complexity of products and services offered by online stores and electronic marketplaces makes the identification of appropriate solutions a challenging task. Customers can differ greatly in their expertise and level of knowledge w.r.t. such product assortments. Consequently, intelligent sales assistance systems are required which support customers with intuitive and personalized dialogs. Knowledge-based recommender systems meet these requirements by allowing a flexible mapping of product, marketing and sales knowledge to the formal representation of a knowledge base. This paper presents the domain-independent knowledge-based recommender system Koba4MS which assists customers and sales representatives by guaranteeing the consistency and appropriateness of proposed solutions, identifying additional selling opportunities and by providing intelligent explanations for identified results. Using examples from the financial services domain we show how constraint satisfaction, model-based diagnosis, personalization and intuitive knowledge acquisition techniques support the effective implementation of customer-oriented sales dialogs. Finally, we present experiences gained from commercial projects.