The frequently changing user preferences and/or item pro-files have put essential importance on the dynamic modeling of users and items in personalized recommender systems. However, due to the insufficiency of per user/item records when splitting the already sparse data across time dimen-sion, previous methods have to restrict the drifting purchas-ing patterns to pre-Assumed distributions, and were hardly able to model them rather directly with, for example, time series analysis. Integrating content information helps to al-leviate the problem in practical systems, but the domain-dependent content knowledge is expensive to obtain due to the large amount of manual efforts. In this paper, we make use of the large volume of textual reviews for the automatic extraction of domain knowledge, namely, the explicit features/aspects in a specific product domain. We thus degrade the product-level modeling of user preferences, which suffers from the lack of data, to the feature-level modeling, which not only grants us the ability to predict user preferences through direct time series analy-sis, but also allows us to know the essence under the surface of product-level changes in purchasing patterns. Besides, the expanded feature space also helps to make cold-start recommendations for users with few purchasing records. Technically, we develop the Fourier-Assisted Auto-Regressive IntegratedMoving Average (FARIMA) process to tackle with the year-long seasonal period of purchasing data to achieve daily-Aware preference predictions, and we leverage the con-ditional opportunity models for daily-Aware personalized rec-ommendation. Extensive experimental results on real-world cosmetic purchasing data from a major e-commerce website (JD.com) in China verified both the effectiveness and effciency of our approach.