Modeling user preference from his historical sequences is one of the core problems of sequential recommendation. Existing methods in this field are widely distributed from conventional methods to deep learning methods. However, most of them only model users' interests within their own sequences and ignore the dynamic collaborative signals among different user sequences, making it insufficient to explore users' preferences. We take inspiration from dynamic graph neural networks to cope with this challenge, modeling the user sequence and dynamic collaborative signals into one framework. We propose a new method named Dynamic Graph Neural Network for Sequential Recommendation (DGSR), which connects different user sequences through a dynamic graph structure, exploring the interactive behavior of users and items with time and order information. Furthermore, we design a Dynamic Graph Recommendation Network to extract user's preferences from the dynamic graph. Consequently, the next-item prediction task in sequential recommendation is converted into a link prediction between the user node and the item node in a dynamic graph. Extensive experiments on four public benchmarks show that DGSR outperforms several state-of-the-art methods. Further studies demonstrate the rationality and effectiveness of modeling user sequences through a dynamic graph.

The ad-hoc retrieval task is to rank related documents given a query and a document collection. A series of deep learning based approaches have been proposed to solve such problem and gained lots of attention. However, we argue that they are inherently based on local word sequences, ignoring the subtle long-distance document-level word relationships. To solve the problem, we explicitly model the document-level word relationship through the graph structure, capturing the subtle information via graph neural networks. In addition, due to the complexity and scale of the document collections, it is considerable to explore the different grain-sized hierarchical matching signals at a more general level. Therefore, we propose a Graph-based Hierarchical Relevance Matching model (GHRM) for ad-hoc retrieval, by which we can capture the subtle and general hierarchical matching signals simultaneously. We validate the effects of GHRM over two representative ad-hoc retrieval benchmarks, the comprehensive experiments and results demonstrate its superiority over state-of-the-art methods.

Text classification is fundamental in natural language processing (NLP), and Graph Neural Networks (GNN) are recently applied in this task.However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words.In this work, to overcome such problems, we propose TextING 1 for inductive text classification via GNN.We first build individual graphs for each document and then use GNN to learn the finegrained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document.Finally, the word nodes are incorporated as the document embedding.Extensive experiments on four benchmark datasets show that our method outperforms state-of-theart text classification methods.

The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems. There exists several kinds of methods proposed in this field, such as Logistic Regression (LR), Factorization Machines (FM) and deep learning based methods like Wide&Deep, Neural Factorization Machines (NFM) and DeepFM. However, such approaches generally use the vector-product of each pair of features, which have ignored the different semantic spaces of the feature interactions. In this paper, we propose a novel Tensor-based Feature interaction Network (TFNet) model, which introduces an operating tensor to elaborate feature interactions via multi-slice matrices in multiple semantic spaces. Extensive offline and online experiments show that TFNet: 1) outperforms the competitive compared methods on the typical Criteo and Avazu datasets; 2) achieves large improvement of revenue and click rate in online A/B tests in the largest Chinese App recommender system, Tencent MyApp.

Working memory (WM) is particularly important for higher cognitive tasks. Previous studies have shown that there are several brain networks under WM task or training, however, it is still unknown how many networks are involved in WM. In this paper, we utilize the method of modularity in the graph theory to explore the module distribution and the degree of coupling of the brain network under the real-time functional magnetic resonance (rtfMRI) WM training. The results suggest that there are four modules under the WM training, and the training changes the modularity of networks significantly. In addition, we further investigate the association between the WM capacity and the modularity and find no significant correlation between the variation of modularity and behavioral changes, which suggests that the modularity of brain network doesn't directly affect the WM performance. Such method of modularity provides a new perspective on network researches under WM.

Description Logic possesses strong knowledge representation and reasoning capabilities and offers logical foundation for Semantic Web ontology languages such as OWL and OWL-S. However, the present implementations of OWL and OWL-S are deficient in sem

Collaborative Web search improves search quality by users' working in cooperation and is a subset of social search. Current Web browsers and search engines provide limited support for it. However, it is easier for experts, who are familiar with some topics, to fulfill they needs through search engines due to their backgrounds, domain knowledge and so on. A sharing experts' experiences approach should be struck based on today's Web browsers and major search engines. This paper presents a convenient way for users to share and utilize experts' experiences through a Web browser toolbar for collaborative Web search. The toolbar an catch search histories and favorites and display recommendations for every user in a popular Web browser through integrating with mainstream search engines like Google, Yahoo!, et al. These collected users' data are uploaded to a recommendation server, in which recommendations are built according to some rules based on an utilizing experts' experiences approach. The toolbar can download some valuable recommendations merging into default search list for prompting a searcher. The core of our proposed approach is a scalable method to measure "to what degree a user is an expert" for a given topic and to detect an expert's experiences based on a hierarchical user profile. Experiments showed that the novel collaborative Web search way is acceptant to users and experts' experiences improved search quality when compared to standard Google rankings. More importantly, results verified our hypothesis that a significant improvement on search quality can be achieved by utilizing experts' experiences.

The description logic, which possesses strong knowledge representation and reasoning capabilities, is the logic basis of the semantic Web ontology languages such as OWL and OWL-S, but OWL and OWL-S are deficient in the semantic modeling of the dynamic services composition and also do not consider the user preferences in the dynamic services composition. The AI planning, which provides an effective method for solving the planning problem and task decomposition in AI, possesses better modeling capability of the action state transformation, but the AI planning is limited in the knowledge representation and reasoning capabilities. Based on the merits of the description logic, OWL-S and the AI planning, this paper extends the OWL-S model, proposes a service composition mechanism and testifies its feasibility in description logic. The results show that this composition mechanism can not only be feasible but also be helpful for the semantic modeling of the services composite process in the semantic Web