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The growing amount of multimedia content is making it hard for end users to find the relevant content. The goal of recommender systems is to assist the users by finding a small subset of relevant multimedia items for each user. Stateoftheart techniques for recommending content are very datacentric. The progress beyond the stateoftheart presented in this book consists in introducing new parameters based on emotions and personality that explain a substantial part of variance in the end users' preferences. The book covers the detection of emotions and personality factors of end users. The book then shows clearly how to use these usercentric data to model end users and thus improve the performance of a recommender system for images. The book will serve as a guideline and inspiration for practitioners and academics in content retrieval and affecting computing.
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birth defects, or other reproductive harm.
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