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.

User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data. Among existing user behavior modeling solutions, attention networks are widely adopted for its effectiveness and relative simplicity. Despite being extensively studied, existing attentions still suffer from two limitations: i) conventional attentions mainly take into account the spatial correlation between user behaviors, regardless the distance between those behaviors in the continuous time space; and ii) these attentions mostly provide a dense and undistinguished distribution over all past behaviors then attentively encode them into the output latent representations. This is however not suitable in practical scenarios where a user’s future actions are relevant to a small subset of her/his historical behaviors. In this paper, we propose a novel attention network, named \textit{self-modulating attention}, that models the complex and non-linearly evolving dynamic user preferences. We empirically demonstrate the effectiveness of our method on top-N sequential recommendation tasks, and the results on three large-scale real-world datasets show that our model can achieve state-of-the-art performance.

For modern recommender systems, the issue of data sparsity still often arises in many cases. To address this challenge, Cross-Domain Recommendation (CDR) has been explored by transferring knowledge learned from the source domain to alleviate the data sparsity in the target domain, and existing methods typically assume intra-domain samples share more commonalities than variabilities. However, it can also cause negative effects when there exists a high diversity among intra-domain samples. In this paper, we propose a novel Mixed Gate Control (MGC) model to fulfill 1) the division of samples into groups according to their commonalities and 2) the transfer of knowledge based on groups. We stack up multiple MGC layers into Multiple Layers MGC (ML-MGC) and apply it to a large-scale bundle retrieval system in a listed internet company. We conduct experiments on its own commercial dataset and two public real-world datasets. Experimental results show the superiority of our model against state-of-the-art methods for the CDR task. Finally, we present a case study to illustrate the groups identified by our model.