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Existing multimedia recommenders suggest a specific type of multimedia items rather than items of different types personalized for a user based on his/her preference. Assume that a user is interested in a particular family movie, it is appealing if a multimedia recommendation system can suggest other movies, music, books, and paintings closely related to the movie. We propose a comprehensive, personalized multimedia recommendation system, denoted MudRecS, which makes recommendations on movies, music, books, and paintings similar in content to other movies, music, books, and/or paintings that a MudRecS user is interested in. MudRecS does not rely on users' access patterns/histories, connection information extracted from social networking sites, collaborated filtering methods, or user personal attributes (such as gender and age) to perform the recommendation task. It simply considers the users' ratings, genres, role players (authors or artists), and reviews of different multimedia items, which are abundant and easy to find on the Web. MudRecS predicts the ratings of multimedia items that match the interests of a user to make recommendations. The performance ofMudRecS has been compared with current state-of-the-art multimedia recommenders using various multimedia datasets, and the experimental results show that MudRecS significantly outperforms other systems in accurately predicting the ratings of multimedia items to be recommended.