Yifan Yang’s research while affiliated with CHINA COMMUNICATIONS CONSTRUCTION and other places

Link prediction is a demanding task in real-world scenarios, such as recommender systems, which targets to predict the unobservable links between different objects by learning network-structured data. In this paper, we propose a novel multi-view graph convolutional neural network (MV-GCN) model to solve this problem based on Matrix Completion method by simultaneously exploiting the interactive relationship and the content information of different objects. Unlike existing approaches directly concatenate the interactive and content information as a single view, the proposed MV-GCN improves the accuracy of the predictions by restricting the consistencies on the graph embedding from multiple views. Experimental results on six primary benchmark datasets, including two homogeneous datasets and four heterogeneous datasets, both show that MV-GCN outperforms the recent state-of-the-art methods.

A certain amount of web traffic is attributed to web bots on the Internet. Web bot traffic has raised serious concerns among website operators, because they usually consume considerable resources at web servers, resulting in high workloads and longer response time, while not bringing in any profit. Even worse, the content of the pages it crawled might later be used for other fraudulent activities. Thus, it is important to detect web bot traffic and characterize it. In this paper, we first propose an efficient approach to detect web bot traffic in a large e-commerce marketplace and then perform an in-depth analysis on the characteristics of web bot traffic. Specifically, our proposed bot detection approach consists of the following modules: (1) an Expectation Maximization (EM)-based feature selection method to extract the most distinguishable features, (2) a gradient based decision tree to calculate the likelihood of being a bot IP, and (3) a threshold estimation mechanism aiming to recover a reasonable amount of non-bot traffic flow. The detection approach has been applied on Taobao/Tmall platforms, and its detection capability has been demonstrated by identifying a considerable amount of web bot traffic. Based on data samples of traffic originating from web bots and normal users, we conduct a comparative analysis to uncover the behavioral patterns of web bots different from normal users. The analysis results reveal their differences in terms of active time, search queries, item and store preferences, and many other aspects. These findings provide new insights for public websites to further improve web bot traffic detection for protecting valuable web contents.

Conducting fraud transactions has become popular among e-commerce sellers to make their products favorable to the platform and buyers, which decreases the utilization efficiency of buyer impressions and jeopardizes the business environment. Fraud detection techniques are necessary but not enough for the platform since it is impossible to recognize all the fraud transactions. In this paper, we focus on improving the platform's impression allocation mechanism to maximize its profit and reduce the sellers' fraudulent behaviors simultaneously. First, we learn a seller behavior model to predict the sellers' fraudulent behaviors from the real-world data provided by one of the largest e-commerce company in the world. Then, we formulate the platform's impression allocation problem as a continuous Markov Decision Process (MDP) with unbounded action space. In order to make the action executable in practice and facilitate learning, we propose a novel deep reinforcement learning algorithm DDPG-ANP that introduces an action norm penalty to the reward function. Experimental results show that our algorithm significantly outperforms existing baselines in terms of scalability and solution quality.