Zhitao Wang

Predicting activity attendance in event-based social networks: content, context and social influence

The newly emerging event-based social networks (EBSNs) connect online and offline social interactions, offering a great opportunity to understand behaviors in the cyber-physical space. While existing efforts have mainly focused on investigating user behaviors in traditional social network services (SNS), this paper aims to exploit individual behaviors in EBSNs, which remains an unsolved problem. In particular, our method predicts activity attendance by discovering a set of factors that connect the physical and cyber spaces and influence individuals attendance of activities in EBSNs. These factors, including content preference, context (spatial and temporal) and social influence, are extracted using different models and techniques. We further propose a novel Singular Value Decomposition with Multi-Factor Neighborhood (SVD-MFN) algorithm to predict activity attendance by integrating the discovered heterogeneous factors into a single framework, in which these factors are fused through a neighborhood set. Experiments based on real-world data from Douban Events demonstrate that the proposed SVD-MFN algorithm outperforms the state-of-the-art prediction methods.

Featuring, Detecting, and Visualizing Human Sentiment in Chinese Micro-Blog

Micro-blog has been increasingly used for the public to express their opinions, and for organizations to detect public sentiment about social events or public policies. In this article, we examine and identify the key problems of this field, focusing particularly on the characteristics of innovative words, multi-media elements, and hierarchical structure of Chinese “Weibo.” Based on the analysis, we propose a novel approach and develop associated theoretical and technological methods to address these problems. These include a new sentiment word mining method based on three wording metrics and point-wise information, a rule set model for analyzing sentiment features of different linguistic components, and the corresponding methodology for calculating sentiment on multi-granularity considering emoticon elements as auxiliary affective factors. We evaluate our new word discovery and sentiment detection methods on a real-life Chinese micro-blog dataset. Initial results show that our new diction can improve sentiment detection, and they demonstrate that our multi-level rule set method is more effective, with the average accuracy being 10.2% and 1.5% higher than two existing methods for Chinese micro-blog sentiment analysis. In addition, we exploit visualization techniques to study the relationships between online sentiment and real life. The visualization of detected sentiment can help depict temporal patterns and spatial discrepancy.

Predictive Network Representation Learning for Link Prediction

In this paper, we propose a predictive network representation learning (PNRL) model to solve the structural link prediction problem. The proposed model defines two learning objectives, i.e., observed structure preservation and hidden link prediction. To integrate the two objectives in a unified model, we develop an effective sampling strategy to select certain edges in a given network as assumed hidden links and regard the rest network structure as observed when training the model. By jointly optimizing the two objectives, the model can not only enhance the predictive ability of node representations but also learn additional link prediction knowledge in the representation space. Experiments on four real-world datasets demonstrate the superiority of the proposed model over the other popular and state-of-the-art approaches.

Sentiment detection and visualization of Chinese micro-blog

Micro-blog has been increasingly used for the public to express their opinions, and for organisations to detect public sentiment about social events. In contrast to the effort and progress made in English-based micro-blog analysis, research on Chinese micro-blog received relatively little attention. In this paper we examine and identify the key problems of this field, focusing particularly on the characteristics of innovative words, emoticon elements and hierarchical structure of Chinese “Weibo”. Based on the analysis we propose and develop associated theoretical and technological methods to address these problems. These include the development of new sentiment word mining method based on three wording standards and point-wise metrics, a rule set model for analyzing sentiment features of different linguistic components, and the corresponding methodology for calculating sentiment on multi-granularity considering emoticon elements. We use original Chinese tweets from a dataset of Sina Weibo to test and evaluate our new word discovery and sentiment detection methods. Initial results show that our new diction can improve sentiment detection, and demonstrate that our multi-level rule set method is more effective by giving 10.2% and 1.5% higher average accuracy than two existing methods for Chinese micro-blog sentiment analysis. In addition, we exploit visualisation techniques to study the relationships between online sentiment and real life, which can help depict the correlation between public emotions and events.

Context-Aware Mobile Web Browsing Based on HTML5

Internet is steaming with a great variety of information including instant news, music, advertisement, etc. However, users will be overwhelmed by too much information presented on web page at once. In this paper, we propose a context-aware mobile web browsing system to reduce user distraction and ultimately enhance user experience. First, we capture user context such as time and location information through sensors embedded in mobile phones. Second, we recommend relevant information and adapt the web page according to user profile and current context. We implement a prototype of context-aware web information access by leveraging HTML5.

Variational Recurrent Model for Session-based Recommendation

Session-based recommendation performance has been significantly improved by Recurrent Neural Networks (RNN). However, existing RNN-based models do not expose the global knowledge of frequent click patterns or consider variability of sequential behaviors in sessions. In this paper, we propose a novel Variational Recurrent Model (VRM), which employs the stochastic latent variable to capture the knowledge of frequent click patterns and impose variability for the sequential behavior modeling. A stochastic generative process of session sequence is specified, where the latent variable modulates the generation of session sequences in RNN. We further extend VRM to a Conditional Variational Recurrent Model (CVRM) by considering additional information (e.g., focused category in sessions) as the generative condition. When evaluated on a public benchmark dataset, VRM and its extension clearly demonstrate their superiority over popular baselines and state-of-the-art models.

A Sequential Neural Information Diffusion Model with Structure Attention

In this paper, we propose a novel sequential neural network with structure attention to model information diffusion. The proposed model explores both sequential nature of an information diffusion process and structural characteristics of user connection graph. The recurrent neural network framework is employed to model the sequential information. The attention mechanism is incorporated to capture the structural dependency among users, which is defined as the diffusion context of a user. A gating mechanism is further developed to effectively integrate the sequential and structural information. The proposed model is evaluated on the diffusion prediction task. The performances on both synthetic and real datasets demonstrate its superiority over popular baselines and state-of-the-art sequence-based models.

Attention Network for Information Diffusion Prediction

In this paper, we propose an attention network for diffusion prediction problem. The developed diffusion attention module can effectively explore the implicit user-to-user diffusion dependency among information cascade users. Besides, the user-to-cascade importance and the time-decay effect are captured and utilized by the model. The superiority of the proposed model over state-of-the-art methods is demonstrated by experiments on real diffusion data.