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This recommendation algorithm based on User-Item Rating Matrix is inefficient in the case of cold-start. The Application of Multi-Attribute Rating Matrix (MARM) can solve the problem effectively. The user and item information are analyzed to create their attribute-tables. The user's ratings are mapped to the relevant item attributes and the user's attributes respectively to generate a User Attribute-Item Attribute Rating Matrix (UAIARM). After UAIARM is simplified, MARM will be created. When a new item/user enters into this system, the attributes of new item/user and MARM are matched to find the N users/item with the highest match degrees as the target of the new items or the recommended items. Experiment results validate the cold-start recommendation algorithm based on MARM is efficient.