Focusing on user remarkable burden and high dimension of Multi-Criteria Collaborative Filtering (MC-CF) recommendation system, the recommendation system of Multi-Attribute Utility Collaborative Filtering (MAU-CF) was proposed. Firstly, attribute weight and attribute-value utility were extracted by user browsing behavior, and user's multi-attribute utility function was built to achieve implicit rating of items. Secondly, attribute-value collection according to user preference was constructed based on Genetic Algorithm (GA). Thirdly, the nearest neighborhood was looked for by attribute weight and attribute-value similarity of attribute-value collection. Finally, utilities of items which the nearest neighborhood had browed and bought would be predicted for user by similarity, and the high-utility items would be recommended to user. In the comparison experiments with MC-CF, the explicit utility was replaced by the implicit utility calculated by MAU-CF, calculation dimension decreased by 44.16%, time expense decreased by 27.36%, and Mean Absolute Error (MAE) decreased by 5.69%, and user satisfaction increased by 13.44%. The experimental results show MAU-CF recommendation system outperforms MC-CF recommendation system on user burden, calculation dimension, and recommendation quality.