Item Splitting has been proposed as a technique for improving Collaborative Filtering (CF) by means of grouping and exploiting ratings according to the contexts in which they were generated. It shows positive effects on recommendation in the presence of significant differences between the users' preferences within distinct contexts. However, the additional user effort and specific system requirements needed to acquire contextual data may hamper the direct application of the above technique. In this paper we propose to split item sets using a number of time context representations derived from easy-to-collect rating timestamps. Initial results on standard datasets show that the proposed time contexts for item splitting let improve recommendation performance of a state-of-the-art CF algorithm in an offline evaluation setting simulating real-world conditions. Copyright 2013 ACM.