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The complexity of the product assortments offered by on-line selling platforms makes selection a challenging task. Customers differ in respect to expertise and product knowledge, but intelligent recommender systems offer personalized dialogues that support the product-selection process. This paper describes CWAdvisor, a domain-independent, knowledge-based recommender environment that provides users with consistently appropriate solutions, identifies additional selling opportunities, and explains solutions. The discussion uses examples from several application domains to show how model-based diagnosis, personalization, and intuitive knowledge-acquisition techniques support customer-oriented sales dialogues. Experience obtained in industrial projects is reported, and successfully deployed recommender applications are evaluated.