Hansu Gu

ETree: Effective and Efficient Event Modeling for Real-Time Online Social Media Networks

Outline social media networks (OSMNs) such as Twitter provide great opportunities for public engagement and event information dissemination. Event-related discussions occur in real time and at the worldwide scale. However, these discussions are in the form of short, unstructured messages and dynamically woven into daily chats and status updates. Compared with traditional news articles, the rich and diverse user-generated content raises unique new challenges for tracking and analyzing events. Effective and efficient event modeling is thus essential for real-time information-intensive OSMNs. In this work, we propose ETree, an effective and efficient event modeling solution for social media network sites. Targeting the unique challenges of this problem, ETree consists of three key components: (1) an n-gram based content analysis technique for identifying core information blocks from a large number of short messages, (2) an incremental and hierarchical modeling technique for identifying and constructing event theme structures at different granularities, and (3) an enhanced temporal analysis technique for identifying inherent causalities between information blocks. Detailed evaluation using 3.5 million tweets over a 5-month period demonstrates that ETree can efficiently generate high-quality event structures and identify inherent causal relationships with high accuracy.

Group-based Latent Dirichlet Allocation (Group-LDA)

Most current book recommendation and marketing strategies in online social media are implemented by creating topics or posting advertisements for the brand. They do not precisely target the audiences who are interested in these books, so the recommendation or marketing quality is not guaranteed. In order to solve this problem, we propose an effective audience detection method based on Group-based Latent Dirichlet Allocation (Group-LDA) in order to precisely detect book audiences. Group-LDA is a new probabilistic topic model derived from Latent Dirichlet Allocation (LDA), which introduces a new latent concept of group to describe the topic relevance among documents by incorporating book module and book chapter information into the model. Group-LDA is evaluated on Weibo.com with fifty popular books randomly sampled from the reading channel on Douban.com. According to the evaluation results, Group-LDA can effectively detect different types of readers for most categories of books. It outperforms LSA, LDA, author-topic model (ATM) and some other collaborative filtering methods in terms of precision, recall, F1-score and MAP for book audience detection.

Behavior prediction using an improved Hidden Markov Model to support people with disabilities in smart homes

Smart environment has been evolved as a hot research topic with the development of machine learning algorithms and wireless communication technologies. Existing smart home solutions usually need inhabitants to operate device controllers directly. However, for people with disabilities, it is inconvenient and difficult to perform such manual operations. Therefore, it is important to develop automatic and intelligent services to reduce operation inconvenience and improve comfort level. In this paper, an Improved Hidden Markov Model (IHMM) is presented to support personalized behavior prediction for people with disabilities. The model can learn behavior patterns of users and provide services to inhabitants automatically. Moreover, by breaking the time invariant hypothesis in Hidden Markov Model, we incorporate time information as positions of states, and develop a temporal state transition matrix to replace the fixed state transition matrix to demonstrate the probabilities of state transitions. As a result, different values of daily temperature sections are characterized and identified as hidden variables, which guide user activities. Evaluation of proposed work has shown that IHMM improves the prediction accuracy by at least 10% compared to the traditional HMM.

Influential user recommendation through SVD based topic diversification

With the development of web 2.0, online community websites have become one of the most interactive ways for disabilities to communicate with others and understand the world. However, new disabled users may sometimes find it difficult to learn about existing communities and the topics they feel interested in are drastically different from normal users. On the other hand, influential users have already developed their impact in different topics within communities and are hence able to help disabilities quickly adapt to the communities. As the result, a method that can recommend influential users in diverse topics is necessary. However, traditional approaches to identify influential users are not very comprehensive and accurate without the consideration of topic diversity. In this paper, we present an evaluation index system and a SVD based influence model which can evaluate influence comprehensively. The discussions in online communities are then classified by the topics that attract disabilities most. Finally, a combined influential users recommendation with diverse topics is proposed. We conduct experiments on an online disability community dataset and results show that our approach significantly outperforms other traditional methods.

AdaError: An Adaptive Learning Rate Method for Matrix Approximation-based Collaborative Filtering

Gradient-based learning methods such as stochastic gradient descent are widely used in matrix approximation-based collaborative filtering algorithms to train recommendation models based on observed user-item ratings. One major difficulty in existing gradient-based learning methods is determining proper learning rates, since model convergence would be inaccurate or very slow if the learning rate is too large or too small, respectively. This paper proposes AdaError, an adaptive learning rate method for matrix approximation-based collaborative filtering. AdaError eliminates the need of manually tuning the learning rates by adaptively adjusting the learning rates based on the noisiness level of user-item ratings, using smaller learning rates for noisy ratings so as to reduce their impact on the learned models. Our theoretical and empirical analysis shows that AdaError can improve the generalization performance of the learned models. Experimental studies on the MovieLens and Netflix datasets also demonstrate that AdaError outperforms state-of-the-art adaptive learning rate methods in matrix approximation-based collaborative filtering. Furthermore, by applying AdaError to the standard matrix approximation method, we can achieve statistically significant improvements over state-of-the-art collaborative filtering methods in both rating prediction accuracy and top-N recommendation accuracy.

Hybrid recommendation model based on incremental collaborative filtering and content-based algorithms

In the past decade, online news consumption has been steadily growing. New articles are published every few minutes, and user preferences are also constantly changing. Traditional recommender systems update model at regular intervals, which cannot adjust recommendation list dynamically according to the changes of user preferences. In this paper, we propose a hybrid recommendation model which contains two key components: incremental update item-based collaborative filtering (CF) and latent semantic analysis based relative term frequency algorithms. The hybrid recommendation model adjusts recommendation list dynamically by updating similarity table of items incrementally in incremental update item-based CF module, moreover, combining collaborative filtering and content-based algorithm ensures the relevance of recommendation articles. Results show that our proposed hybrid recommendation model outperforms traditional recommender approaches.

Credit scoring using ensemble classification based on variable weighting clustering

Credit scoring plays an important role in financial institutions and debt based crowdfunding platforms as well as peer to peer lending platforms. In the last few years, adopting ensemble methods for credit scoring has become much more popular. However, the performance of ensemble methods is easily affected by the parameter settings and the number of base classifiers. Ensemble classification based on clustering is able to determine the best number of base classifiers automatically by clustering and find optimal parameter settings for base classifiers by training them individually on the training subsets combined by clusters. By this way, the adverse effect of manually setting the parameters and the number of base classifiers can be avoided. However, the different contributions of attributes to the distance metrics are not considered in conventional clustering methods, which may decrease the performance of ensemble classifiers based on them. Moreover, unbalanced training subsets decrease the performance of base classifiers, which results in the bad performance of ensemble classifiers. In our approach, to address the above problems, we first assign different weights to different variables when measuring the distance between two instances in the clustering step, and then adopt Subagging resampling method to deal with unbalanced training subsets in the training process. Experimental results show that our approach can improve the performance of the ensemble classifier.