OpenRaiser/Intern-Atlas huggingface dataset:
Query and Animate Multi-attribute Trajectory Data
Authors: Jianqiu Xu, Ralf Hartmut Güting
Venue: CIKM
Year: 2017

The widespread use of GPS-enabled devices has led to huge amounts of trajectory data. In addition to location and time, trajectories are associated with descriptive attributes representing different aspects of real entities, called multi-attribute trajectories. This comes from the combination of several data sources and enables a range of new applications in which users can find interesting trajectories and discover potential relationships that cannot be determined solely based on GPS data. In this demo, we provide the motivation scenario and introduce a system that is developed to integrate standard trajectories (a sequence of timestamped locations) and attributes into one unified framework. The system is able to answer a range of interesting queries on multi-attribute trajectories that are not handled by standard trajectories. The system supports both standard trajectories and multi-attribute trajectories. We demonstrate how to form queries and animate multi-attribute trajectories in the system. To our knowledge, existing moving objects prototype systems do not support multi-attribute trajectories.
Learning to Rank with Query-level Semi-supervised Autoencoders
Authors: Bo Xu, Hongfei Lin, Yuan Lin, Kan Xu
Venue: CIKM
Year: 2017

Learning to rank utilizes machine learning methods to solve ranking problems by constructing ranking models in a supervised way, which needs fixed-length feature vectors of documents as inputs, and outputs the ranking models learned by iteratively reducing the pre-defined ranking loss. The document features are always extracted based on classic textual statistics, and different features contribute differently to ranking performance. Given that well-defined features would contribute more to the retrieval performance, we investigate the usage of autoencoders to enrich the feature representations of documents. Autoencoders, as basic building blocks of deep neural networks, have been successfully used in many text mining tasks for generating effective features. To enrich the feature space for learning to rank, we introduce supervision into the loss functions of autoencoders. Specifically, we first train a linear ranking model on the training data, and then incorporate the learned weights into the reconstruction costs of an autoencoder. Meanwhile, we accumulate the costs of documents for a given query with query-level constraints for producing more useful features. We evaluate the effectiveness of our model on three LETOR datasets, and show that our model can generate effective document features to improve the retrieval performance.
MultiSentiNet: A Deep Semantic Network for Multimodal Sentiment Analysis
Authors: Nan Xu, Wenji Mao
Venue: CIKM
Year: 2017

With the prevalence of more diverse and multiform user-generated content in social networking sites, multimodal sentiment analysis has become an increasingly important research topic in recent years. Previous work on multimodal sentiment analysis directly extracts feature representation of each modality and fuse these features for classification. Consequently, some detailed semantic information for sentiment analysis and the correlation between image and text have been ignored. In this paper, we propose a deep semantic network, namely MultiSentiNet, for multimodal sentiment analysis. We first identify object and scene as salient detectors to extract deep semantic features of images. We then propose a visual feature guided attention LSTM model to extract words that are important to understand the sentiment of whole tweet and aggregate the representation of those informative words with visual semantic features, object and scene. The experiments on two public available sentiment datasets verify the effectiveness of our MultiSentiNet model and show that our extracted semantic features demonstrate high correlations with human sentiments.
Attentive Graph-based Recursive Neural Network for Collective Vertex Classification
Authors: Qiongkai Xu, Qing Wang, Chenchen Xu, Lizhen Qu
Venue: CIKM
Year: 2017

Vertex classification is a critical task in graph analysis, where both contents and linkage of vertices are incorporated during classification. Recently, researchers proposed using deep neural network to build an end-to-end framework, which can capture both local content and structure information. These approaches were proved effective in incorporating semantic meanings of neighbouring vertices, while the usefulness of this information was not properly considered. In this paper, we propose an Attentive Graph-based Recursive Neural Network (AGRNN), which exerts attention on neural network to make our model focus on vertices with more relevant semantic information. We evaluated our approach on three real-world datasets and also datasets with synthetic noise. Our experimental results show that AGRNN achieves the state-of-the-art performance, in terms of effectiveness and robustness. We have also illustrated some attention weight samples to demonstrate the rationality of our model.
Select Your Questions Wisely: For Entity Resolution With Crowd Errors
Authors: Vijaya Krishna Yalavarthi, Xiangyu Ke, Arijit Khan
Venue: CIKM
Year: 2017

Crowdsourcing is becoming increasingly important in entity resolution tasks due to their inherent complexity such as clustering of images and natural language processing. Humans can provide more insightful information for these difficult problems compared to machine-based automatic techniques. Nevertheless, human workers can make mistakes due to lack of domain expertise or seriousness, ambiguity, or even due to malicious intents. The bulk of literature usually deals with human errors via majority voting or by assigning a universal error rate over crowd workers. However, such approaches are incomplete, and often inconsistent, because the expertise of crowd workers are diverse with possible biases, thereby making it largely inappropriate to assume a universal error rate for all workers over all crowdsourcing tasks. We mitigate the above challenges by considering an uncertain graph model, where the edge probability between two records A and B denotes the ratio of crowd workers who voted YES on the question if A and B are same entity. To reflect independence across different crowdsourcing tasks, we apply the notion of possible worlds, and develop parameter-free algorithms for both next crowdsourcing and entity resolution tasks. In particular, for next crowdsourcing, we identify the record pair that maximally increases the reliability of the current clustering. Since reliability takes into account the connected-ness inside and across all clusters, this metric is more effective in deciding next questions, in comparison with state-of-the-art works, which consider local features, such as individual edges, paths, or nodes to select next crowdsourcing questions. Based on detailed empirical analysis over real-world datasets, we find that our proposed solution, PERC (probabilistic entity resolution with imperfect crowd) improves the quality by 15% and reduces the overall cost by 50% for the crowdsourcing-based entity resolution.
Understanding Database Performance Inefficiencies in Real-world Web Applications
Authors: Cong Yan, Alvin Cheung, Junwen Yang, Shan Lu
Venue: CIKM
Year: 2017

Many modern database-backed web applications are built upon Object Relational Mapping (ORM) frameworks. While such frame- works ease application development by abstracting persistent data as objects, such convenience comes with a performance cost. In this paper, we studied 27 real-world open-source applications built on top of the popular Ruby on Rails ORM framework, with the goal to understand the database-related performance inefficiencies in these applications. We discovered a number of inefficiencies rang- ing from physical design issues to how queries are expressed in the application code. We applied static program analysis to identify and measure how prevalent these issues are, then suggested techniques to alleviate these issues and measured the potential performance gain as a result. These techniques significantly reduce database query time (up to 91%) and the webpage response time (up to 98%). Our study provides guidance to the design of future database engines and ORM frameworks to support database application that are performant yet without sacrificing programmability.
On Migratory Behavior in Video Consumption
Authors: Huan Yan, Tzu-Heng Lin, Gang Wang, Yong Li, Haitao Zheng, Depeng Jin, Ben Y. Zhao
Venue: CIKM
Year: 2017

Today's video streaming market is crowded with various content providers (CPs). For individual CPs, understanding user behavior, in particular how users migrate among different CPs, is crucial for improving users' on-site experience and the CP's chance of success. In this paper, we take a data-driven approach to analyze and model user migration behavior in video streaming, i.e., users switching content provider during active sessions. Based on a large ISP dataset over two months (6 major content providers, 3.8 million users, and 315 million video requests), we study common migration patterns and reasons of migration. We find that migratory behavior is prevalent: 66% of users switch CPs with an average switching frequency of 13%. In addition, migration behaviors are highly diverse: regardless large or small CPs, they all have dedicated groups of users who like to switch to them for certain types of videos. Regarding reasons of migration, we find CP service quality rarely causes migration, while a few popular videos play a bigger role. Nearly 60% of cross-site migrations are landed to 0.14% top videos. Finally, we validate our findings by building an accurate regression model to predict user migration frequency, and discuss the implications of our results to CPs.
When Deep Learning Meets Transfer Learning
Authors: Qiang Yang
Venue: CIKM
Year: 2017

Deep learning has achieved great success as evidenced by many practical applications and contests. However, deep learning developed so far also has some inherent limitations. In particular, deep learning is not yet very adaptable to different related domains and cannot handle small data. In this talk, I will give an overview of how transfer learning can help alleviate these problems. In particular, I will present some recent progress on integrating deep learning and transfer learning together and show some interesting applications in sentiment analysis, image processing and urban computing.
From Properties to Links: Deep Network Embedding on Incomplete Graphs
Authors: Dejian Yang, Senzhang Wang, Chaozhuo Li, Xiaoming Zhang, Zhoujun Li
Venue: CIKM
Year: 2017

As an effective way of learning node representations in networks, network embedding has attracted increasing research interests recently. Most existing approaches use shallow models and only work on static networks by extracting local or global topology information of each node as the algorithm input. It is challenging for such approaches to learn a desirable node representation on incomplete graphs with a large number of missing links or on dynamic graphs with new nodes joining in. It is even challenging for them to deeply fuse other types of data such as node properties into the learning process to help better represent the nodes with insufficient links. In this paper, we for the first time study the problem of network embedding on incomplete networks. We propose a Multi-View Correlation-learning based Deep Network Embedding method named MVC-DNE to incorporate both the network structure and the node properties for more effectively and efficiently perform network embedding on incomplete networks. Specifically, we consider the topology structure of the network and the node properties as two correlated views. The insight is that the learned representation vector of a node should reflect its characteristics in both views. Under a multi-view correlation learning based deep autoencoder framework, the structure view and property view embeddings are integrated and mutually reinforced through both self-view and cross-view learning. As MVC-DNE can learn a representation mapping function, it can directly generate the representation vectors for the new nodes without retraining the model. Thus it is especially more efficient than previous methods. Empirically, we evaluate MVC-DNE over three real network datasets on two data mining applications, and the results demonstrate that MVC-DNE significantly outperforms state-of-the-art methods.
Nationality Classification Using Name Embeddings
Authors: Junting Ye, Shuchu Han, Yifan Hu, Baris Coskun, Meizhu Liu, Hong Qin, Steven Skiena
Venue: CIKM
Year: 2017

Nationality identification unlocks important demographic information, with many applications in biomedical and sociological research. Existing name-based nationality classifiers use name substrings as features and are trained on small, unrepresentative sets of labeled names, typically extracted from Wikipedia. As a result, these methods achieve limited performance and cannot support fine-grained classification. We exploit the phenomena of homophily in communication patterns to learn name embeddings, a new representation that encodes gender, ethnicity, and nationality which is readily applicable to building classifiers and other systems. Through our analysis of 57M contact lists from a major Internet company, we are able to design a fine-grained nationality classifier covering 39 groups representing over 90% of the world population. In an evaluation against other published systems over 13 common classes, our F1 score (0.795) is substantial better than our closest competitor Ethnea (0.580). To the best of our knowledge, this is the most accurate, fine-grained nationality classifier available. As a social media application, we apply our classifiers to the followers of major Twitter celebrities over six different domains. We demonstrate stark differences in the ethnicities of the followers of Trump and Obama, and in the sports and entertainments favored by different groups. Finally, we identify an anomalous political figure whose presumably inflated following appears largely incapable of reading the language he posts in.
Tone Analyzer for Online Customer Service: An Unsupervised Model with Interfered Training
Authors: Peifeng Yin, Zhe Liu, Anbang Xu, Taiga Nakamura
Venue: CIKM
Year: 2017

Emotion analysis of online customer service conservation is important for good user experience and customer satisfaction. However, conventional metrics do not fit this application scenario. In this work, by collecting and labeling online conversations of customer service on Twitter, we identify 8 new metrics, named as tones, to describe emotional information. To better interpret each tone, we extend the Latent Dirichlet Allocation (LDA) model to Tone LDA (T-LDA). In T-LDA, each latent topic is explicitly associated with one of three semantic categories, i.e., tone-related, domain-specific and auxiliary. By integrating tone label into learning, T-LDA can interfere the original unsupervised training process and thus is able to identify representative tone-related words. In evaluation, T-LDA shows better performance than baselines in predicting tone intensity. Also, a case study is conducted to analyze each tone via T-LDA output.
Low-Rank Matrix Completion over Finite Abelian Group Algebras for Context-Aware Recommendation
Authors: Chia-An Yu, Tak-Shing Chan, Yi-Hsuan Yang
Venue: CIKM
Year: 2017

The incorporation of contextual information is an important part of context-aware recommendation. Many context-aware recommendation systems adopt tensor completion to include contextual information. However, the symmetries between dimensions of a tensor induce an unreasonable assumption that users, items and contexts should be treated equally in recommender systems. In this paper, we address this by using matrices over finite abelian group algebra (AGA) to model context-aware interactions between users and items. Specifically, we formulate context-aware recommendation as a low-rank matrix completion problem over AGA (MC-AGA) and derive a new algorithm using the inexact augmented Lagrange multiplier method. We then test MC-AGA on two real-world datasets: one containing implicit feedback and one with explicit feedback. Experiment results show that MC-AGA outperforms not only existing tensor completion algorithms but also recommendation systems with other context-aware representations.
Fast Algorithms for Pareto Optimal Group-based Skyline
Authors: Wenhui Yu, Zheng Qin, Jinfei Liu, Li Xiong, Xu Chen, Huidi Zhang
Venue: CIKM
Year: 2017

Skyline, aiming at finding a Pareto optimal subset of points in a multi-dimensional dataset, has gained great interest due to its extensive use for multi-criteria analysis and decision making. Skyline consists of all points that are not dominated by, or not worse than other points. It is a candidate set of optimal solution, which depends on a specific evaluation criterion for optimum. However, conventional skyline queries, which return individual points, are inadequate in group querying case since optimal combinations are required. To address this gap, we study the skyline computation in group case and propose fast methods to find the group-based skyline (G-skyline), which contains Pareto optimal groups. For computing the front k skyline layers, we lay out an efficient approach that does the search concurrently on each dimension and investigates each point in subspace. After that, we present a novel structure to construct the G-skyline with a queue of combinations of the first-layer points. Experimental results show that our algorithms are several orders of magnitude faster than the previous work.
Detecting Multiple Periods and Periodic Patterns in Event Time Sequences
Authors: Quan Yuan, Jingbo Shang, Xin Cao, Chao Zhang, Xinhe Geng, Jiawei Han
Venue: CIKM
Year: 2017

Periodicity is prevalent in physical world, and many events involve more than one periods, eg individual's mobility, tide pattern, and massive transportation utilization. Knowing the true periods of events can benefit a number of applications, such as traffic prediction, time-aware recommendation and advertisement, and anomaly detection. However, detecting multiple periods is a very challenging task due to not only the interwoven periodic patterns but also the low quality of event tracking records. In this paper, we study the problem of discovering all true periods and the corresponded occurring patterns of an event from a noisy and incomplete observation sequence. We devise a novel scoring function, by maximizing which we can identify the true periodic patterns involved in the sequence. We prove that, however, optimizing the objective function is an NP-hard problem. To address this challenge, we develop a heuristic algorithm named Timeslot Coverage Model (TiCom), for identifying the periods and periodic patterns approximately. The results of extensive experiments on both synthetic and real-life datasets show that our model outperforms the state-of-the-art baselines significantly in various tasks, including period detection, periodic pattern identification, and anomaly detection.
Spectrum-based Deep Neural Networks for Fraud Detection
Authors: Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu
Venue: CIKM
Year: 2017

In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as spectral coordinate) in the low dimensional spectral space of the graph's adjacency matrix as the input of deep neural networks. Spectral coordinates in the spectral space capture the most useful topology information of the network. Due to the small dimension of spectral coordinates (compared with the dimension of the adjacency matrix derived from a graph), training deep neural networks becomes feasible. We develop and evaluate two neural networks, deep autoencoder and convolutional neural network, in our fraud detection framework. Experimental results on a real signed graph show that our spectrum based deep neural networks are effective in fraud detection.
MIKE: Keyphrase Extraction by Integrating Multidimensional Information
Authors: Yuxiang Zhang, Yaocheng Chang, Xiaoqing Liu, Sujatha Das Gollapalli, Xiaoli Li, Chunjing Xiao
Venue: CIKM
Year: 2017

Traditional supervised keyphrase extraction models depend on the features of labelled keyphrases while prevailing unsupervised models mainly rely on structure of the word graph, with candidate words as nodes and edges capturing the co-occurrence information between words. However, systematically integrating all these multidimensional heterogeneous information into a unified model is relatively unexplored. In this paper, we focus on how to effectively exploit multidimensional information to improve the keyphrase extraction performance (MIKE). Specifically, we propose a random-walk parametric model, MIKE, that learns the latent representation for a candidate keyphrase that captures the mutual influences among all information, and simultaneously optimizes the parameters and ranking scores of candidates in the word graph. We use the gradient-descent algorithm to optimize our model and show the comprehensive experiments with two publicly-available WWW and KDD datasets in Computer Science. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art graph-based keyphrase extraction approaches.
BL-ECD: Broad Learning based Enterprise Community Detection via Hierarchical Structure Fusion
Authors: Jiawei Zhang, Limeng Cui, Philip S. Yu, Yuanhua Lv
Venue: CIKM
Year: 2017

Employees in companies can be divided into different social communities, and those who frequently socialize with each other will be treated as close friends and are grouped in the same community. In the enterprise context, a large amount of information about the employees is available in both (1) offline company internal sources and (2) online enterprise social networks (ESNs). Each of the information sources also contain multiple categories of employees' socialization activities at the same time. In this paper, we propose to detect the social communities of the employees in companies based on the broad learning setting with both these online and offline information sources simultaneously, and the problem is formally called the "Broad Learning based Enterprise Community Detection" (BL-ECD) problem. To address the problem, a novel broad learning based community detection framework named "HeterogeneoUs Multi-sOurce ClusteRing" (HUMOR) is introduced in this paper. Based on the various enterprise social intimacy measures introduced in this paper, HUMOR detects a set of micro community structures of the employees based on each of the socialization activities respectively. To obtain the (globally) consistent community structure of employees in the company, HUMOR further fuses these micro community structures via two broad learning phases: (1) intra-fusion of micro community structures to obtain the online and offline (locally) consistent communities respectively, and (2) inter-fusion of the online and offline communities to achieve the (globally) consistent community structure of employees. Extensive experiments conducted on real-world enterprise datasets demonstrate our method can perform very well in addressing the BL-ECD problem.