Abstract: Information recommendation system usually involve a multitask problem, which tries to predict not only users' click-through rate (CTR) but also the post-click conversion rate (CVR). At the same time, for multi-functional information systems, there are commonly multiple services for users, such as news feed, search engine, and product suggestions. The prediction/ranking model should be conducted in a multi-scene manner. In the present patent document, embodiments of a unified ranking model for such a multi-task and multi-scene problem are disclosed. The disclosed model explores independent and non-shared embeddings for each task and scene, which reduces the coupling between tasks and scenes. Therefore, new tasks or scenes may be added easily. Besides, a simplified network may be chosen beyond the embedding layer, which largely improves the ranking efficiency for various online services. Extensive offline and online experiments demonstrated the superiority of model embodiments.