Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
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This paper presents an approach to how speaker classification can be used to enable new ways for recommender systems in a mobile shopping environment to bootstrap user models and avoid common problems such as the "early rater". In a concrete shopping scenario, we introduce the speech-controlled Mobile ShopAssist demonstrator that allows a new customer to more quickly find a product that fulfills his or her demographic group's specific requirements by exploiting features extracted from speech using the Agenderspeaker classification system. We propose a method for computing preference scores based on the user's profile and demonstrate how the application's GUI can be adapted to deliver the recommendations to the user.