Abstract
This position paper discusses a novel recommender system for e-commerce in virtual reality environments. The system provides recommendations by taking into account prepurchase ratings in addition to traditional postpurchase ratings. Users' positive emotions are captured in the form of electroencephalogram (EEG) signals while interacting with 3D virtual products prior to purchase. The prepurchase ratings are calculated from the averaged relative power of the collected EEG signals. Prepurchase ratings are complementary to postpurchase ratings and help in alleviating two severe issues that traditional recommender systems suffer from: data sparsity and cold start. By making proper use of both pre- and postpurchase ratings, user preference can be modeled more accurately. This will improve the effectiveness of the current recommender systems and may change the traditional e- business applications.
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