Sequential recommendation is one of the main tasks in recommender systems, where the next action (e.g., purchase, visit, and click) of the user is predicted based on his/her past sequence of actions. Translating Embeddings is a knowledge graph completion approach which was recently adapted to a translation-based sequential recommendation (TransRec) method. We observe a flaw of TransRec when handling complex translations, which hinders it from generating accurate suggestions. In view of this, we propose a translation-based recommender for complex users (CTransRec), which utilizes category-specific projection and temporal dynamic relaxation . Using our proposed Margin-based Pairwise Bayesian Personalized Ranking and Time-Aware Negative Sampling , CTransRec outperforms state-of-the-art methods for sequential recommendation on extremely sparse data. The superiority of CTransRec, which is confirmed by our extensive experiments on both public data and real data obtained from the industry, comes from not only the additional information used in training but also the fact that CTransRec makes good use of this additional information to model the complex translations.