Intelligent Data Analysis - Volume 22, issue 6

Authors: Liu, Huiting | Yang, Liangquan | Ling, Chao | Wu, Xindong

Article Type: Research Article

Abstract: Recently how to recommend celebrities to public has become an interesting problem in real social network applications. In this problem, a matrix of users’ following actions is usually very sparse. Therefore, it causes conventional collaborative filtering based methods to degrade significantly in recommendation performance. To address the sparsity problem, side information could be rendered. Collaborative social topic regression (CSTR) is an appealing new method, which combines the matrix of general users’ following actions, content information and a social network of celebrities. However, this method is limited by using the topic model latent Dirichlet allocation (LDA) as the critical component. The …learned content representation may not be compact and effective enough. Moreover, the social network of general users also exists, which is helpful for recommendation. In this paper, we employ a deep learning component to learn more effective feature representation and incorporate social network information of general users by adding social regularization terms. We propose a novel hierarchical Bayesian model named collaborative social deep learning (CSDL), which jointly handles deep learning for the content information and collaborative filtering for general users’ following actions, the social network of celebrities and that of general users. Experiments on two real-world datasets show the effectiveness of our proposed model. Show more

Keywords: Celebrity recommendation, deep learning, social network, text mining

DOI: 10.3233/IDA-173674

Citation: Intelligent Data Analysis, vol. 22, no. 6, pp. 1375-1394, 2018

Authors: Kim, Misuk | Park, Eunjeong Lucy | Cho, Sungzoon

Article Type: Research Article

Abstract: Many researches have exploited textual data, such as news, online blogs, and financial reports, in order to predict stock price movements effectively. Previous studies formed the task as a classification problem predicting upward or downward movement of stock prices from text documents. Such an approach, however, may be deemed inappropriate when combined with sentiment analysis. In financial documents, same words may convey different sentiments across different sectors; if documents from multiple sectors are learned simultaneously, performance can deteriorate. Therefore, we conducted sentiment analysis of 8-K financial reports of firms sector by sector. In particular, we also employed distributed representation for …predicting stock price movements. Experiment results show that our approach improves prediction performance by 25.4% over the baseline model, and that the direction of post-announcement stock price movements shifts accordingly with the polarity of the sentiment of reports. Not only does our model improve predictability, but also provides visualizations, which may assist agents actively trading in the field with understanding the drivers for the observed stock movements. The two main aspects of our model, predictability and interpretability, will provide meaningful insights to help decision-makers in the industry with time-split trading decisions or data-driven detection of promising companies. Show more

Keywords: Stock price change prediction, distributed representation, sentiment analysis, visualization, 8-K financial reports

DOI: 10.3233/IDA-173670

Citation: Intelligent Data Analysis, vol. 22, no. 6, pp. 1395-1413, 2018

Authors: Wang, Xiaohan | Luo, Yonglong | Jiang, Yefeng | Wu, Wenli | Yu, Qingying

Article Type: Research Article

Abstract: Data needs to be released to the relevant decision makers and researchers. Privacy protection should be carried out first because it contains personal sensitive information. The k -anonymity algorithm is an important privacy protection algorithm, and partitioning is one of its key methods. To reduce the computational complexity and low speed of existing privacy-preserving algorithms for high-dimensional data publishing, a probabilistic optimal projection partition k -dimensional (KD)-tree k -anonymity algorithm is proposed. First, some attribute dimensions are probabilistically selected from the global domain. Then, for these dimensions, the partition coefficient is calculated and the …optimal partition point is determined. Furthermore, an improved KD-tree structure is introduced in which a node is a collection rather than a data point. The proposed KD-tree node is divided into left and right child nodes by the hyper-plane passing through the dividing point and perpendicular to the optimal dimension. The proposed algorithm is validated by a theoretical analysis and comparison experiments. The results show that the proposed algorithm can reduce the average generalization range by 11% to 22% compared to traditional k -anonymity. This enables better division and better dataset availability. Moreover, the runtime is reduced by 8% to 32% compared to globally optimal projection partitioning k -anonymity. Show more

Keywords: Data publishing, privacy protection, k-anonymity, KD-tree, probabilistic partitioning

DOI: 10.3233/IDA-173589

Citation: Intelligent Data Analysis, vol. 22, no. 6, pp. 1415-1437, 2018