Scalable Dynamic User Preferences for Recommender Systems through the Use of the Well-Founded Semantics

User modeling and personalization are the key aspects of recommender systems in terms of recommendation quality. ERASP is an add-on to existing recommender systems which uses dynamic logic programming -- an extension of answer set programming -- as a means for users to specify and update their models and preferences, with the purpose of enhancing recommendations. While being an excellent solution in recommender systems limited to a few thousand products, ERASP does not scale well beyond that point. In this paper we present a major theoretical redesign of ERASP which entails a significant improvement in the performance of its implementation, making it usable in domains with hundreds of thousands of products.