OpenRaiser/Intern-Atlas huggingface dataset:
Understanding and Predicting Weight Loss with Mobile Social Networking Data
Authors: Zhiwei Wang, Tyler Derr, Dawei Yin, Jiliang Tang
Venue: CIKM
Year: 2017

It has become increasingly popular to use mobile social networking applications for weight loss and management. Users can not only create profiles and maintain their records but also perform a variety of social activities that shatter the barrier to share or seek information. Due to the open and connected nature, these applications produce massive data that consists of rich weight-related information which offers immense opportunities for us to enable advanced research on weight loss. In this paper, we conduct the initial investigation to understand weight loss with a large-scale mobile social networking dataset with near 10 million users. In particular, we study individual and social factors related to weight loss and reveal a number of interesting findings that help us build a meaningful model to predict weight loss automatically. The experimental results demonstrate the effectiveness of the proposed model and the significance of social factors in weight loss.
Improving the Gain of Visual Perceptual Behaviour on Topic Modeling for Text Recommendation
Authors: Cheng Wang, Yujuan Fang, Zheng Tan, Yuan He
Venue: CIKM
Year: 2017

Internet information services have been greatly improved profiting from the growing performance of interest mining technology. Visual perceptual behaviours, a new hotspot of mining user's interests, have resulted in great gains in some typical Internet information services, e.g., information retrieval and recommendation. It is validated that combining the subjective visual perceptual behaviours with the objective contents can significantly improve these services' performance. However, the existing methods usually treat the contents and visual perceptual behaviours as two independent parts in the calculating process. The gain of visual perceptual behaviours has not been fully exploited. In this paper, we mainly aim at improving the gain of visual perceptual behaviour for text recommendation, by integrating the objective contents with subjective visual perceptual behaviours. We investigate the correlation between user's reading interests and records of real-time interaction on texts, and then design a real-time visual perceptual behaviour based method for text recommendation, which is able to: (1) build a joint interest model, called ViP-LDA (Visual Perceptual LDA), by integrating the user's visual perceptual behaviours into topic model; (2) make more accurate text recommendation based on ViP-LDA with feedback adjustment. Several experiments on a real data set are implemented to demonstrate the effectiveness of our method.
From Fingerprint to Footprint: Revealing Physical World Privacy Leakage by Cyberspace Cookie Logs
Authors: Huandong Wang, Chen Gao, Yong Li, Zhi-Li Zhang, Depeng Jin
Venue: CIKM
Year: 2017

It is well-known that online services resort to various cookies to track users through users' online service identifiers (IDs) - in other words, when users access online services, various "fingerprints" are left behind in the cyberspace. As they roam around in the physical world while accessing online services via mobile devices, users also leave a series of "footprints" -- i.e., hints about their physical locations - in the physical world. This poses a potent new threat to user privacy: one can potentially correlate the "fingerprints" left by the users in the cyberspace with "footprints" left in the physical world to infer and reveal leakage of user physical world privacy, such as frequent user locations or mobility trajectories in the physical world - we refer to this problem as user physical world privacy leakage via user cyberspace privacy leakage. In this paper we address the following fundamental question: what kind - and how much - of user physical world privacy might be leaked if we could get hold of such diverse network datasets even without any physical location information. In order to conduct an in-depth investigation of these questions, we utilize the network data collected via a DPI system at the routers within one of the largest Internet operator in Shanghai, China over a duration of one month. We decompose the fundamental question into the three problems: i) linkage of various online user IDs belonging to the same person via mobility pattern mining; ii) physical location classification via aggregate user mobility patterns over time; and iii) tracking user physical mobility. By developing novel and effective methods for solving each of these problems, we demonstrate that the question of user physical world privacy leakage via user cyberspace privacy leakage is not hypothetical, but indeed poses a real potent threat to user privacy.
A Matrix-Vector Recurrent Unit Model for Capturing Compositional Semantics in Phrase Embeddings
Authors: Rui Wang, Wei Liu, Chris McDonald
Venue: CIKM
Year: 2017

The meaning of a multi-word phrase not only depends on the meaning of its constituent words, but also the rules of composing them to give the so-called compositional semantic. However, many deep learning models for learning compositional semantics target specific NLP tasks such as sentiment classification. Consequently, the word embeddings encode the lexical semantics, the weights of the networks are optimised for the classification task. Such models have no mechanisms to explicitly encode the compositional rules, and hence they are insufficient in capturing the semantics of phrases. We present a novel recurrent computational mechanism that specifically learns the compositionality by encoding the compositional rule of each word into a matrix. The network uses a recurrent architecture to capture the order of words for phrases with various lengths without requiring extra preprocessing such as part-of-speech tagging. The model is thoroughly evaluated on both supervised and unsupervised NLP tasks including phrase similarity, noun-modifier questions, sentiment distribution prediction, and domain specific term identification tasks. We demonstrate that our model consistently outperforms the LSTM and CNN deep learning models, simple algebraic compositions, and other popular baselines on different datasets.
Distant Meta-Path Similarities for Text-Based Heterogeneous Information Networks
Authors: Chenguang Wang, Yangqiu Song, Haoran Li, Yizhou Sun, Ming Zhang, Jiawei Han
Venue: CIKM
Year: 2017

Measuring network similarity is a fundamental data mining problem. The mainstream similarity measures mainly leverage the structural information regarding to the entities in the network without considering the network semantics. In the real world, the heterogeneous information networks (HINs) with rich semantics are ubiquitous. However, the existing network similarity doesn't generalize well in HINs because they fail to capture the HIN semantics. The meta-path has been proposed and demonstrated as a right way to represent semantics in HINs. Therefore, original meta-path based similarities (e.g., PathSim and KnowSim) have been successful in computing the entity proximity in HINs. The intuition is that the more instances of meta-path(s) between entities, the more similar the entities are. Thus the original meta-path similarity only applies to computing the proximity of two neighborhood (connected) entities. In this paper, we propose the distant meta-path similarity that is able to capture HIN semantics between two distant (isolated) entities to provide more meaningful entity proximity. The main idea is that even there is no shared neighborhood entities of (i.e., no meta-path instances connecting) the two entities, but if the more similar neighborhood entities of the entities are, the more similar the two entities should be. We then find out the optimum distant meta-path similarity by exploring the similarity hypothesis space based on different theoretical foundations. We show the state-of-the-art similarity performance of distant meta-path similarity on two text-based HINs and make the datasets public available.
A Study of Feature Construction for Text-based Forecasting of Time Series Variables
Authors: Yiren Wang, Dominic Seyler, Shubhra Kanti Karmaker Santu, ChengXiang Zhai
Venue: CIKM
Year: 2017

Time series are ubiquitous in the world since they are used to measure various phenomena (e.g., temperature, spread of a virus, sales, etc.). Forecasting of time series is highly beneficial (and necessary) for optimizing decisions, yet is a very challenging problem; using only the historical values of the time series is often insufficient. In this paper, we study how to construct effective additional features based on related text data for time series forecasting. Besides the commonly used n-gram features, we propose a general strategy for constructing multiple topical features based on the topics discovered by a topic model. We evaluate feature effectiveness using a data set for predicting stock price changes where we constructed additional features from news text articles for stock market prediction. We found that: 1) Text-based features outperform time series-based features, suggesting the great promise of leveraging text data for improving time series forecasting. 2) Topic-based features are not very effective stand-alone, but they can further improve performance when added on top of n-gram features. 3) The best topic-based feature appears to be a long-term aggregation of topics over time with high weights on recent topics.
Integrating Side Information for Boosting Machine Comprehension
Authors: Yutong Wang, Yixin Xu, Min Yang, Zhou Zhao, Jun Xiao, Yueting Zhuang
Venue: CIKM
Year: 2017

Machine Reading and Comprehension recently has drawn a fair amount of attention in the field of natural language processing. In this paper, we consider integrating side information to improve machine comprehension on answering cloze-style questions more precisely. To leverage the external information, we present a novel attention-based architecture which could feed the side information representations into word level embeddings to explore the comprehension performance. Our experiments show consistent improvements of our model over various baselines.
Interactive Analytics System for Exploring Outliers
Authors: Mingrui Wei, Lei Cao, Chris Cormier, Hui Zheng, Elke A. Rundensteiner
Venue: CIKM
Year: 2017

ONION is the first system with rich interactive support for efficiently analyzing outliers. ONION features an innovative exploration model that offers an "outlier-centric panorama'' into big datasets. The ONION system is composed of an offline preprocessing phase followed by an online exploration phase that supports rich classes of novel exploration operations. As our demonstration illustrates, this enables analysts to interactively explore outliers at near real-time speed even over large datasets. We demonstrate ONION's capabilities with urban planning applications use cases on the Open Street Maps dataset.
Unsupervised Feature Selection with Heterogeneous Side Information
Authors: Xiaokai Wei, Bokai Cao, Philip S. Yu
Venue: CIKM
Year: 2017

Compared to supervised feature selection, unsupervised feature selection tends to be more challenging due to the lack of guidance from class labels. Along with the increasing variety of data sources, many datasets are also equipped with certain side information of heterogeneous structure. Such side information can be critical for feature selection when class labels are unavailable. In this paper, we propose a new feature selection method, SideFS, to exploit such rich side information. We model the complex side information as a heterogeneous network and derive instance correlations to guide subsequent feature selection. Representations are learned from the side information network and the feature selection is performed in a unified framework. Experimental results show that the proposed method can effectively enhance the quality of selected features by incorporating heterogeneous side information.
Crowdsourced Selection on Multi-Attribute Data
Authors: Xueping Weng, Guoliang Li, Huiqi Hu, Jianhua Feng
Venue: CIKM
Year: 2017

Crowdsourced selection asks the crowd to select entities that satisfy a query condition, e.g., selecting the photos of people wearing sunglasses from a given set of photos. Existing studies focus on a single query predicate and in this paper we study the crowdsourced selection problem on multi-attribute data, e.g., selecting the female photos with dark eyes and wearing sunglasses. A straightforward method asks the crowd to answer every entity by checking every predicate in the query. Obviously, this method involves huge monetary cost. Instead, we can select an optimized predicate order and ask the crowd to answer the entities following the order. Since if an entity does not satisfy a predicate, we can prune this entity without needing to ask other predicates and thus this method can reduce the cost. There are two challenges in finding the optimized predicate order. The first is how to detect the predicate order and the second is to capture correlation among different predicates. To address this problem, we propose predicate order based framework to reduce monetary cost. Firstly, we define an expectation tree to store selectivities on predicates and estimate the best predicate order. In each iteration, we estimate the best predicate order from the expectation tree, and then choose a predicate as a question to ask the crowd. After getting the result of the current predicate, we choose next predicate to ask until we get the result. We will update the expectation tree using the answer obtained from the crowd and continue to the next iteration. We also study the problem of answering multiple queries simultaneously, and reduce its cost using the correlation between queries. Finally, we propose a confidence based method to improve the quality. The experiment result shows that our predicate order based algorithm is effective and can reduce cost significantly compared with baseline approaches.
An Empirical Study of Community Overlap: Ground-truth, Algorithmic Solutions, and Implications
Authors: Joyce Jiyoung Whang
Venue: CIKM
Year: 2017

In real-world social networks, communities tend to be overlapped with each other because a vertex can belong to multiple communities. To identify these overlapping communities, a number of overlapping community detection methods have been proposed over the recent years. However, there have been very few studies on the characteristics and the implications of the community overlap. In this paper, we investigate the properties of the nodes and the edges placed within the overlapped regions between the communities using the ground-truth communities as well as algorithmic communities derived from the state-of-the-art overlapping community detection methods. We find that the overlapped nodes and the overlapped edges play different roles from the ones that are not in the overlapped regions. Using real-world data, we empirically show that the highly overlapped nodes are involved in structure holes of a network. Also, we show that the overlapped nodes and edges play an important role in forming new links in evolving networks and diffusing information through a network.
Non-Exhaustive, Overlapping Co-Clustering
Authors: Joyce Jiyoung Whang, Inderjit S. Dhillon
Venue: CIKM
Year: 2017

The goal of co-clustering is to simultaneously identify a clustering of the rows as well as the columns of a two dimensional data matrix. Most existing co-clustering algorithms are designed to find pairwise disjoint and exhaustive co-clusters. However, many real-world datasets might contain not only a large overlap between co-clusters but also outliers which should not belong to any co-cluster. We formulate the problem of Non-Exhaustive, Overlapping Co-Clustering where both of the row and column clusters are allowed to overlap with each other and the outliers for each dimension of the data matrix are not assigned to any cluster. To solve this problem, we propose an intuitive objective function, and develop an efficient iterative algorithm which we call the NEO-CC algorithm. We theoretically show that the NEO-CC algorithm monotonically decreases the proposed objective function. Experimental results show that the NEO-CC algorithm is able to effectively capture the underlying co-clustering structure of real-world data, and thus outperforms state-of-the-art clustering and co-clustering methods.
Simulating Zero-Resource Spoken Term Discovery
Authors: Jerome White, Douglas W. Oard
Venue: CIKM
Year: 2017

If search engines are ever to index all of the spoken content in the world, they will need to handle hundreds of languages for which no automatic speech recognition systems exist. Zero-resource spoken term discovery, in which repeated content is detected in some acoustic representation, offers a potentially useful source of indexing features. This paper describes a text-based simulation of a zero-resource spoken term discovery system that allows any information retrieval test collection to be used as a basis for early development of information retrieval techniques. It is proposed that these techniques can be later applied to actual zero-resource spoken term discovery results.
Does That Mean You're Happy?: RNN-based Modeling of User Interaction Sequences to Detect Good Abandonment
Authors: Kyle Williams, Imed Zitouni
Venue: CIKM
Year: 2017

Queries for which there are no clicks are known as abandoned queries. Differentiating between good and bad abandonment queries has become an important task in search engine evaluation since it allows for better measurement of search engine features that do not require users to click. Examples of these features include answers on the SERP and detailed Web result snippets. In this paper, we investigate how sequences of user interactions on the SERP differ between good and bad abandonment. To do this, we study the behavior patterns on a labeled dataset of abandoned queries and find that they differ in several ways, such as in the number of user interactions and the nature of those interactions. Based on this insight, we frame good abandonment detection as a sequence classification problem. We use a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) to model the sequence of user interactions and show that it performs significantly better than other baselines when detecting good abandonment, achieving 71% accuracy. Our findings have implications for search engine evaluation.
Additional Workshops Co-located with CIKM 2017
Authors: Marianne Winslett
Venue: CIKM
Year: 2017

Summary of three workshops co-located with CIKM 2017.
Conflict of Interest Declaration and Detection System in Heterogeneous Networks
Authors: Siyuan Wu, Leong Hou U, Sourav S. Bhowmick, Wolfgang Gatterbauer
Venue: CIKM
Year: 2017

Peer review is the most critical process in evaluating an article to be accepted for publication in an academic venue. When assigning a reviewer to evaluate an article, the assignment should be aware of conflicts of interest (COIs) such that the reviews are fair to everyone. However, existing conference management systems simply ask reviewers and authors to declare their explicit COIs through a plain search user interface guided by some simple conflict rules. We argue that such declaration system is not enough to discover all latent COI cases. In this work, we study a graphical declaration system that visualizes the relationships of authors and reviewers based on a heterogeneous co-authorship network. With the help of the declarations, we attempt to detect the latent COIs automatically based on the meta-paths of a heterogeneous network.
A New Approach to Compute CNNs for Extremely Large Images
Authors: Sai Wu, Mengdan Zhang, Gang Chen, Ke Chen
Venue: CIKM
Year: 2017

CNN (Convolution Neural Network) is widely used in visual analysis and achieves exceptionally high performances in image classification, face detection, object recognition, image recoloring, and other learning jobs. Using deep learning frameworks, such as Torch and Tensorflow, CNN can be efficiently computed by leveraging the power of GPU. However, one drawback of GPU is its limited memory which prohibits us from handling large images. Passing a 4K resolution image to the VGG network will result in an exception of out-of-memory for Titan-X GPU. In this paper, we propose a new approach that adopts the BSP (bulk synchronization parallel) model to compute CNNs for images of any size. Before fed to a specific CNN layer, the image is split into smaller pieces which go through the neural network separately. Then, a specific padding and normalization technique is adopted to merge sub-images back into one image. Our approach can be easily extended to support distributed multi-GPUs. In this paper, we use neural style network as our example to illustrate the effectiveness of our approach. We show that using one Titan-X GPU, we can transfer the style of an image with 10,000×10,000 pixels within 1 minute.
Common-Specific Multimodal Learning for Deep Belief Network
Authors: Changsheng Xiang, Xiaoming Jin
Venue: CIKM
Year: 2017

Multimodal Deep Belief Network has been widely used to extract representations for multimodal data by fusing the high-level features of each data modality into common representations. Such straightforward fusion strategy can benefit the classification and information retrieval tasks. However, it may introduce noise in case the high-level features are not naturally common hence non-fusable for different modalities. Intuitively, each modality may have its own specific features and corresponding representation capabilities thus should not be simply fused. Therefore, it is more reasonable to fuse only the common features and represent the multimodal data by both the fused features and the modality-specific features. To distinguish common features from modal-specific features is a challenging task for traditional DBN models where all features are crudely mixed. This paper proposes the Common-Specific Multimodal Deep Belief Network (CSDBN) to solve the problem. CS-DBN automatically separates common features from modal-specific features and fuses only the common ones for data representation. Experimental results demonstrate the superiority of CS-DBN for classification tasks compared with the baseline approaches.
Automatic Navbox Generation by Interpretable Clustering over Linked Entities
Authors: Chenhao Xie, Lihan Chen, Jiaqing Liang, Kezun Zhang, Yanghua Xiao, Hanghang Tong, Haixun Wang, Wei Wang
Venue: CIKM
Year: 2017

Rare efforts have been devoted to generating the structured Navigation Box (Navbox) for Wikipedia articles. A Navbox is a table in Wikipedia article page that provides a consistent navigation system for related entities. Navbox is critical for the readership and editing efficiency of Wikipedia. In this paper, we target on the automatic generation of Navbox for Wikipedia articles. Instead of performing information extraction over unstructured natural language text directly, an alternative avenue is explored by focusing on a rich set of semi-structured data in Wikipedia articles: linked entities. The core idea of this paper is as follows: If we cluster the linked entities and interpret them appropriately, we can construct a high-quality Navbox for the article entity. We propose a clustering-then-labeling algorithm to realize the idea. Experiments show that the proposed solutions are effective. Ultimately, our approach enriches Wikipedia with 1.95 million new Navboxes of high quality.