Modeling user preferences and user dynamics is of greatest importance to build efficient recommender systems. Existing methods capture the sequential dynamics of a user using fixed-order Markov chains. We propose to use frequent sequences to identify the important part of user history and use a unified metric model to embed items based on user preferences and dynamics. Experiments demonstrate the advantages of this approach.