Mining Trust Relationships from Online Social Networks

With the growing popularity of online social network, trust plays a more and more important role in connecting people to each other. We rely on our personal trust to accept recommendations, to make purchase decisions and to select transaction partners in the online community. Therefore, how to obtain trust relationships through mining online social networks becomes an important research topic. There are several shortcomings of existing trust mining methods. First, trust is category-dependent. However, most of the methods overlook the category attribute of trust relationships, which leads to low accuracy in trust calculation. Second, since the data in online social networks cannot be understood and processed by machines directly, traditional mining methods require much human effort and are not easily applied to other applications. To solve the above problems, we propose a semantic-based trust reasoning mechanism to mine trust relationships from online social networks automatically. We emphasize the category attribute of pairwise relationships and utilize Semantic Web technologies to build a domain ontology for data communication and knowledge sharing. We exploit role-based and behavior-based reasoning functions to infer implicit trust relationships and category-specific trust relationships. We make use of path expressions to extend reasoning rules so that the mining process can be done directly without much human effort. We perform experiments on real-life data extracted from Epinions. The experimental results verify the effectiveness and wide application use of our proposed method.

Branch-Activated Multi-Domain Convolutional Neural Network for Visual Tracking

Convolutional neural networks(CNNs) have been applied in state-of-the-art visual tracking tasks to represent the target. However, most existing algorithms treat visual tracking as an object-specific task. Therefore,the model needs to be retrained for different test video sequences. We propose a branch-activated multi-domain convolutional neural network(BAMDCNN). In contrast to most existing trackers based on CNNs which require frequent online training, BAMDCNN only needs offline training and online fine-tuning. Specifically, BAMDCNN exploits category-specific features that are more robust against variations. To allow for learning category-specific information, we introduce a group algorithm and a branch activation method. Experimental results on challenging benchmark show that the proposed algorithm outperforms other state-of-the-art methods. What's more, compared with CNN based trackers, BAMDCNN increases tracking speed.