Ranking ensemble is a critical component in real recommender systems. When a user visits a platform, the system will prepare several item lists, each of which is generally from a single behavior objective recommendation model. As multiple behavior intents, e.g., both clicking and buying some specific item category, are commonly concurrent in a user visit, it is necessary to integrate multiple single-objective ranking lists into one. However, previous work on rank aggregation mainly focused on fusing homogeneous item lists with the same objective while ignoring ensemble of heterogeneous lists ranked with different objectives with various user intents.

Temporal point process (TPP) is a powerful tool to model the generation of irregular event sequences. With the rise of deep learning, neural TPPs have been proposed and made a great success. In neural TPPs, a state is extracted to characterize the influence of past events and updated accordingly when a new event is observed. Existing works can be categorized into two frameworks; each has pros and cons. Thus, we integrate these frameworks to overcome their shortages. To propose a model under the integrated framework, we first propose a novel and theoretically well-founded continuous-time layer normalized gated recurrent unit (CLNGRU) model, which is simpler, more robust, and more expressive than a pioneering work. Then we propose an enhanced version of CLNGRU (CLNGRU-E) under the integrated framework. We conduct experiments on synthetic and real datasets. Experimental results show the superiority of our models against the state-of-the-art methods on both generative and predictive tasks. Finally, we show the advantages of our models in recovering the underlying generative processes from sampled sequences.