Sequential interaction networks (SIN) have been commonly adopted in many applications such as recommendation systems, search engines and social networks to describe the mutual influence between users and items/products. Efforts on representing SIN are mainly focused on capturing the dynamics of networks in Euclidean space, and recently plenty of work has extended to hyperbolic geometry for implicit hierarchical learning. Previous approaches which learn the embedding trajectories of users and items achieve promising results. However, there are still a range of fundamental issues remaining open. For example, is it appropriate to place user and item nodes in one identical space regardless of their inherent discrepancy? Instead of residing in a single fixed curvature space, how will the representation spaces evolve when new interaction occurs?

Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users' behavior information during the aligning procedure and thus still suffer from poor learning performance. In fact, we observe that social network alignment and user behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment and user behavior analysis problem in this paper. We design a novel framework named BANANA-RGB. In this framework, to capture users' multi-scale behavior information in each social network, we train a variant of the hierarchical periodic memory network with personalized memorization. To leverage behavior analysis for social network alignment, we design a tensor fusion network-based alignment component to improve the performance. To further leverage social network alignment for behavior analysis, we design a gating-based cross-network behavior fusion component to integrate users' behavior information in different social networks based on the alignment result. We iteratively train the above two components to make the two tasks benefit from each other. Extensive experiments on real-world datasets demonstrate that our proposed approach outperforms the state-of-the-art methods.

As an essential component of task-oriented dialogue systems, slot filling requires enormous labeled training data in a certain domain.However, in most cases, there is little or no target domain training data is available in the training stage.Thus, cross-domain slot filling has to cope with the data scarcity problem by zero/few-shot learning.Previous researches on zero/few-shot cross-domain slot filling focus on slot descriptions and examples while ignoring the slot type ambiguity and example ambiguity issues.To address these problems, we propose Abundant Information Slot Filling Generator (AISFG), a generative model with a novel query template that incorporates domain descriptions, slot descriptions, and examples with context.Experimental results show that our model outperforms state-of-the-art approaches in zero/few-shot slot filling task. 1

Knowledge graph embedding aims to learn representations of entities and relations in low-dimensional space. Recently, extensive studies combine the characteristics of knowledge graphs with different geometric spaces, including Euclidean space, complex space, hyperbolic space and others, which achieves significant progress in representation learning. However, existing methods are subject to at least one of the following limitations: 1) ignoring the uncertainty, 2) incapability of complex relation patterns. To address the above issues simultaneously, we propose a novel model named DiriE, which embeds entities as Dirichlet distributions and relations as multinomial distributions. DiriE employs Bayesian inference to measure the relations between entities and learns binary embeddings of knowledge graphs for modeling complex relation patterns. Additionally, we propose a two-step negative triple generation method that generates negative triples of both entities and relations. We conduct a solid theoretical analysis to demonstrate the effectiveness and robustness of our method, including the expressiveness of complex relation patterns and the ability to model uncertainty. Furthermore, extensive experiments show that our method outperforms state-of-the-art methods in link prediction on benchmark datasets.

User profiling is playing an increasingly important role in real-world applications. Previous works have shown that integrating user information from multiple social networks helps to significantly improve the performance of user profiling. However, these studies either ignore the different contributions of various features in different profiling tasks or need to train one model for each task. What’s more, the assumption of the strong relatedness between user profiling tasks limits their application. These phenomena make inferring comprehensive user attributes still an open problem. In this paper, we propose a novel method, called Hierarchical Attention-based Model with sparse-sharing-based muLti-task sElf-Training algorithm (HAMLET), for comprehensive user profiling. More specifically, we first employ a hierarchical attention-based network as our base network to represent users. It assigns various features from different social networks with different weights for different users during the fusing procedure. Then, we propose a multi-task self-training algorithm that takes advantage of both task correlations and self-training to obtain better performance. We conduct extensive experiments on two real-world datasets and verify the superiority of HAMLET for user profiling.