OpenRaiser/Intern-Atlas huggingface dataset:
SIMD-Based Multiple Sets Intersection with Dual-Scale Search Algorithm
Authors: Xingshen Song, Yuexiang Yang, Xiaoyong Li
Venue: CIKM
Year: 2017

Conjunctive Boolean query is one fundamental operation for document retrieval in many information systems and databases. Various algorithms have been put up in terms of maximizing the query efficiency. In recent years, researchers began to exploit the parallel advantage of single-instruction-multiple-data (SIMD) instructions to accelerate the intersection procedure and achieved substantial gains over previous scalar algorithms. However, these works only focus on intersecting two sets at a time and ignore the scenario of multiple sets intersection. We present a flexible search algorithm which balances non-SIMD and SIMD comparisons in order to provide efficient and effective intersection.
Soft Seeded SSL Graphs for Unsupervised Semantic Similarity-based Retrieval
Authors: Avikalp Srivastava, Madhav Datt
Venue: CIKM
Year: 2017

Semantic similarity based retrieval is playing an increasingly important role in many IR systems such as modern web search, question-answering, similar document retrieval etc. Improvements in retrieval of semantically similar content are very significant to applications like Quora, Stack Overflow, Siri etc. We propose a novel unsupervised model for semantic similarity based content retrieval, where we construct semantic flow graphs for each query, and introduce the concept of "soft seeding" in graph based semi-supervised learning (SSL) to convert this into an unsupervised model. We demonstrate the effectiveness of our model on an equivalent question retrieval problem on the Stack Exchange QA dataset, where our unsupervised approach significantly outperforms the state-of-the-art unsupervised models, and produces comparable results to the best supervised models. Our research provides a method to tackle semantic similarity based retrieval without any training data, and allows seamless extension to different domain QA communities, as well as to other semantic equivalence tasks.
How Safe is Your (Taxi) Driver?
Authors: Rade Stanojevic
Venue: CIKM
Year: 2017

For an auto insurer, understanding the risk of individual drivers is a critical factor in building a healthy and profitable portfolio. For decades, assessing the risk of drivers has relied on demographic information which allows the insurer to segment the market in several risk groups priced with an appropriate premium. In the recent years, however, some insurers started experimenting with so called Usage-Based Insurance (UBI) in which the insurer monitors a number of additional variables (mostly related to the location) and uses them to better assess the risk of the drivers. While several studies have reported results on the UBI trials these studies keep the studied data confidential (for obvious privacy and business concerns) which inevitably limits their reproducibility and interest by the data-mining community. In this paper we discuss a methodology for studying driver risk assessment using a public dataset of 173M taxi rides in NYC with over 40K drivers. Our approach for risk assessment utilizes not only the location data (which is significantly sparser than what is normally exploited in UBI) but also the revenue, tips and overall activity of the drivers (as proxies of their behavioral traits) and obtain risk scoring accuracy on par with the reported results on non-professional driver cohorts in spite of sparser location data and no demographic information about the drivers.
Hierarchical Module Classification in Mixed-initiative Conversational Agent System
Authors: Sia Xin Yun Suzanna, Anthony Lianjie Li
Venue: CIKM
Year: 2017

Our operational context is a task-oriented dialog system where no single module satisfactorily addresses the range of conversational queries from humans. Such systems must be equipped with a range of technologies to address semantic, factual, task-oriented, open domain conversations using rule-based, semantic-web, traditional machine learning and deep learning. This raises two key challenges. First, the modules need to be managed and selected appropriately. Second, the complexity of troubleshooting on such systems is high. We address these challenges with a mixed-initiative model that controls conversational logic through hierarchical classification. We also developed an interface to increase interpretability for operators and to aggregate module performance.
Sentence Retrieval with Sentiment-specific Topical Anchoring for Review Summarization
Authors: Jiaxing Tan, Alexander Kotov, Rojiar Pir Mohammadiani, Yumei Huo
Venue: CIKM
Year: 2017

We propose Topic Anchoring-based Review Summarization (TARS), a two-step extractive summarization method, which creates review summaries from the sentences that represent the most important aspects of a review. In the first step, the proposed method utilizes Topic Aspect Sentiment Model (TASM), a novel sentiment-topic model, to identify aspects of sentiment-specific topics in a collection of reviews. The output of TASM is utilized in the second step of TARS to rank review sentences based on how representative of the most important review aspects their words are. Qualitative and quantitative evaluation of review summaries using two collections indicate the effectiveness of structuring review summaries around aspects of sentiment-specific topics.
Representation Learning of Large-Scale Knowledge Graphs via Entity Feature Combinations
Authors: Zhen Tan, Xiang Zhao, Wei Wang
Venue: CIKM
Year: 2017

Knowledge graphs are typical large-scale multi-relational structures, which comprise a large amount of fact triplets. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is widely used to embed fact triplets into low-dimensional spaces. Many existing knowledge graph embedding models either focus on learning rich features from entities but fail to extract good features of relations, or employ sophisticated models that have rather high time and memory-space complexities. In this paper, we propose a novel knowledge graph embedding model, CombinE. It exploits entity features from two complementary perspectives via the plus and minus combinations. We start with the plus combination, where we use shared features of entity pairs participating in a relation to convey its relation features. To also allow differences of each pairs of entities participating in a relation, we also use the minus combination, where we concentrate on individual entity features, and regard relations as a channel to offset the divergence and preserve the prominence between head and tail entities. Compared with the state-of-the-art models, our experimental results demonstrate that CombinE outperforms existing ones and has low time and memory-space complexities.
QALink: Enriching Text Documents with Relevant Q&A Site Contents
Authors: Yixuan Tang, Weilong Huang, Qi Liu, Anthony K. H. Tung, Xiaoli Wang, Jisong Yang, Beibei Zhang
Venue: CIKM
Year: 2017

With rapid development of Q&A sites such as Quora and StackExchange, high quality question-answer pairs have been produced by users. These Q&A contents cover a wide range of topics, and they are useful for users to resolve queries and obtain new knowledge. Meanwhile, when people are reading digital documents, they may encounter reading problems such as lack of background information and unclear illustration of concepts. We believe that Q&A sites offer high-quality contents which can serve as rich supplements to digital documents. In this paper, we devise a rigorous formulation of the novel text enrichment problem, and design an end-to-end system named QALink which assigns the most relevant Q&A contents to the corresponding section of the document. We first present a new segmentation approach to model each document with a hierarchical structure. Based on the hierarchy, queries are constructed to retrieve and rank related question-answer pairs. Both syntactical and semantic features are adopted in our system. The empirical evaluation results indicate that QALink is able to effectively enrich text documents with relevant Q&A contents to help people better understand the documents.
Dyadic Memory Networks for Aspect-based Sentiment Analysis
Authors: Yi Tay, Luu Anh Tuan, Siu Cheung Hui
Venue: CIKM
Year: 2017

This paper proposes Dyadic Memory Networks (DyMemNN), a novel extension of end-to-end memory networks (memNN) for aspect-based sentiment analysis (ABSA). Originally designed for question answering tasks, memNN operates via a memory selection operation in which relevant memory pieces are adaptively selected based on the input query. In the problem of ABSA, this is analogous to aspects and documents in which the relationship between each word in the document is compared with the aspect vector. In the standard memory networks, simple dot products or feed forward neural networks are used to model the relationship between aspect and words which lacks representation learning capability. As such, our dyadic memory networks ameliorates this weakness by enabling rich dyadic interactions between aspect and word embeddings by integrating either parameterized neural tensor compositions or holographic compositions into the memory selection operation. To this end, we propose two variations of our dyadic memory networks, namely the Tensor DyMemNN and Holo DyMemNN. Overall, our two models are end-to-end neural architectures that enable rich dyadic interaction between aspect and document which intuitively leads to better performance. Via extensive experiments, we show that our proposed models achieve the state-of-the-art performance and outperform many neural architectures across six benchmark datasets.
Multi-Task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs
Authors: Yi Tay, Luu Anh Tuan, Minh C. Phan, Siu Cheung Hui
Venue: CIKM
Year: 2017

Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-discrete attributes for each entity. Intuitively, these attributes such as height, price or population count are able to richly characterize entities in knowledge graphs. This additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-discrete attribute information in a relational setting. Specifically, we train a neural network for triplet prediction along with a separate network for attribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and attributes that encode information about both tasks. Moreover, such attributes are not only central to many predictive tasks as an information source but also as a prediction target. Therefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly attractive as well. We show that our approach outperforms many state-of-the-art methods for the tasks of relational triplet classification and attribute value prediction.
Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning
Authors: Xian Teng, Yu-Ru Lin, Xidao Wen
Venue: CIKM
Year: 2017

Detecting anomalous patterns from dynamic and multi-attributed network systems has been a challenging problem due to the complication of temporal dynamics and the variations reflected in multiple data sources. We propose a Multi-view Time-Series Hypersphere Learning (MTHL) approach that leverages multi-view learning and support vector description to tackle this problem. Given a dynamic network with time-varying edge and node properties, MTHL projects multi-view time-series data into a shared latent subspace, and then learns a compact hypersphere surrounding normal samples with soft constraints. The learned hypersphere allows for effectively distinguishing normal and abnormal cases. We further propose an efficient, two-stage alternating optimization algorithm as a solution to the MTHL. Extensive experiments are conducted on both synthetic and real datasets. Results demonstrate that our method outperforms the state-of-the-art baseline methods in detecting three types of events that involve (i) time-varying features alone, (ii) time-aggregated features alone, as well as (iii) both features. Moreover, our approach exhibits consistent and good performance in face of issues including noises, anomaly pollution in training phase and data imbalance.
Visualizing Deep Neural Networks with Interaction of Super-pixels
Authors: Shixin Tian, Ying Cai
Venue: CIKM
Year: 2017

An effective way to visualize the prediction of deep neural networks on an image is to decompose the prediction into the contribution of units (pixels or patches). In the existing works, these units are largely considered independently, thus limiting the performance of visualization. In this paper, we propose a new predication visualization method that uses super-pixel as a contribution unit. Moreover, our method takes into consideration of the interaction of adjacent super-pixels. We implement our technique and evaluate its performance with various images. Our results show its excellent performance.
Collecting Non-Geotagged Local Tweets via Bandit Algorithms
Authors: Saki Ueda, Yuto Yamaguchi, Hiroyuki Kitagawa
Venue: CIKM
Year: 2017

How can we collect non-geotagged tweets posted by users in a specific location as many as possible in a limited time span? How can we find such users if we do not have much information about the specified location? Although there are varieties of methods to estimate the locations of users, these methods are not directly applicable to this problem because they require collecting a large amount of random tweets and then filter them to obtain a small amount of tweets from such users. In this paper, we propose a framework that incrementally finds such users and continuously collects tweets from them. Our framework is based on the bandit algorithm that adjusts the trade-off between exploration and exploitation, in other words, it simultaneously finds new users in the specified location and collects tweets from already-found users. The experimental results show that the bandit algorithm works well on this problem and outperforms the carefully-designed baselines.
BoostVHT: Boosting Distributed Streaming Decision Trees
Authors: Theodore Vasiloudis, Foteini Beligianni, Gianmarco De Francisci Morales
Venue: CIKM
Year: 2017

Online boosting improves the accuracy of classifiers for unbounded streams of data by chaining them into an ensemble. Due to its sequential nature, boosting has proven hard to parallelize, even more so in the online setting. This paper introduces BoostVHT, a technique to parallelize online boosting algorithms. Our proposal leverages a recently-developed model-parallel learning algorithm for streaming decision trees as a base learner. This design allows to neatly separate the model boosting from its training. As a result, BoostVHT provides a flexible learning framework which can employ any existing online boosting algorithm, while at the same time it can leverage the computing power of modern parallel and distributed cluster environments. We implement our technique on Apache SAMOA, an open-source platform for mining big data streams that can be run on several distributed execution engines, and demonstrate order of magnitude speedups compared to the state-of-the-art.
Extracting Records from the Web Using a Signal Processing Approach
Authors: Roberto Panerai Velloso, Carina F. Dorneles
Venue: CIKM
Year: 2017

Extracting records from web pages enables a number of important applications and has immense value due to the amount and diversity of available information that can be extracted. This problem, although vastly studied, remains open because it is not a trivial one. Due to the scale of data, a feasible approach must be both automatic and efficient (and of course effective). We present here a novel approach, fully automatic and computationally efficient, using signal processing techniques to detect regularities and patterns in the structure of web pages. Our approach segments the web page, detects the data regions within it, identifies the records boundaries and aligns the records. Results show high f-score and linearithmic time complexity behaviour.
Blockchain-based Data Management and Analytics for Micro-insurance Applications
Authors: Hoang Tam Vo, Lenin Mehedy, Mukesh K. Mohania, Ermyas Abebe
Venue: CIKM
Year: 2017

In this paper, we demonstrate a blockchain-based solution for transparently managing and analyzing data in a pay-as-you-go car insurance application. This application allows drivers who rarely use cars to only pay insurance premium for particular trips they would like to travel. One of the key challenges from database perspective is how to ensure all the data pertaining to the actual trip and premium payment made by the users are transparently recorded so that every party in the insurance contract including the driver, the insurance company, and the financial institution is confident that the data are tamper-proof and traceable.
Data Driven Chiller Plant Energy Optimization with Domain Knowledge
Authors: Hoang Dung Vu, Kok-Soon Chai, Bryan D. Keating, Nurislam Tursynbek, Boyan Xu, Kaige Yang, Xiaoyan Yang, Zhenjie Zhang
Venue: CIKM
Year: 2017

Refrigeration and chiller optimization is an important and well studied topic in mechanical engineering, mostly taking advantage of physical models, designed on top of over-simplified assumptions, over the equipments. Conventional optimization techniques using physical models make decisions of online parameter tuning, based on very limited information of hardware specifications and external conditions, e.g., outdoor weather. In recent years, new generation of sensors is becoming essential part of new chiller plants, for the first time allowing the system administrators to continuously monitor the running status of all equipments in a timely and accurate way. The explosive growth of data flowing to databases, driven by the increasing analytical power by machine learning and data mining, unveils new possibilities of data-driven approaches for real-time chiller plant optimization. This paper presents our research and industrial experience on the adoption of data models and optimizations on chiller plant and discusses the lessons learnt from our practice on real world plants. Instead of employing complex machine learning models, we emphasize the incorporation of appropriate domain knowledge into data analysis tools, which turns out to be the key performance improver over state-of-the-art deep learning techniques by a significant margin. Our empirical evaluation on a real world chiller plant achieves savings by more than 7% on daily power consumption.
Attributed Signed Network Embedding
Authors: Suhang Wang, Charu C. Aggarwal, Jiliang Tang, Huan Liu
Venue: CIKM
Year: 2017

The major task of network embedding is to learn low-dimensional vector representations of social-network nodes. It facilitates many analytical tasks such as link prediction and node clustering and thus has attracted increasing attention. The majority of existing embedding algorithms are designed for unsigned social networks. However, many social media networks have both positive and negative links, for which unsigned algorithms have little utility. Recent findings in signed network analysis suggest that negative links have distinct properties and added value over positive links. This brings about both challenges and opportunities for signed network embedding. In addition, user attributes, which encode properties and interests of users, provide complementary information to network structures and have the potential to improve signed network embedding. Therefore, in this paper, we study the novel problem of signed social network embedding with attributes. We propose a novel framework SNEA, which exploits the network structure and user attributes simultaneously for network representation learning. Experimental results on link prediction and node clustering with real-world datasets demonstrate the effectiveness of SNEA.
Coupled Sparse Matrix Factorization for Response Time Prediction in Logistics Services
Authors: Yuqi Wang, Jiannong Cao, Lifang He, Wengen Li, Lichao Sun, Philip S. Yu
Venue: CIKM
Year: 2017

Nowadays, there is an emerging way of connecting logistics orders and van drivers, where it is crucial to predict the order response time. Accurate prediction of order response time would not only facilitate decision making on order dispatching, but also pave ways for applications such as supply-demand analysis and driver scheduling, leading to high system efficiency. In this work, we forecast order response time on current day by fusing data from order history and driver historical locations. Specifically, we propose Coupled Sparse Matrix Factorization (CSMF) to deal with the heterogeneous fusion and data sparsity challenges raised in this problem. CSMF jointly learns from multiple heterogeneous sparse data through the proposed weight setting mechanism therein. Experiments on real-world datasets demonstrate the effectiveness of our approach, compared to various baseline methods. The performances of many variants of the proposed method are also presented to show the effectiveness of each component.
Using Knowledge Graphs to Explain Entity Co-occurrence in Twitter
Authors: Yiwei Wang, Mark James Carman, Yuan-Fang Li
Venue: CIKM
Year: 2017

Modern Knowledge Graphs such as DBPedia contain significant information regarding Named Entities and the logical relationships which exist between them. Twitter on the other hand, contains important information on the popularity and frequency with which these entities are mentioned and discussed in combination with one another. In this paper we investigate whether these two sources of information can be used to complement and explain one another. In particular, we would like to know whether the logical relationships (a.k.a. semantic paths) which exist between pairs of known entities can help to explain the frequency with which those entities co-occur with one another in Twitter. To do this we train a ranking function over semantic paths between pairs of entities. The aim of the ranker is to identify the path that most likely explains why a particular pair of entities have appeared together in a particular tweet. We train the ranking model using a number of lexical, graph-embedding and popularity-based features over semantic paths containing a single intermediate entity and demonstrate the efficacy of the model for determining why pairs of entities occur together in tweets.
CleanCloud: Cleaning Big Data on Cloud
Authors: Hongzhi Wang, Xiaoou Ding, Xiangying Chen, Jianzhong Li, Hong Gao
Venue: CIKM
Year: 2017

We describe CleanCloud, a system for cleaning big data based on Map-Reduce paradigm in cloud. Using Map-Reduce paradigm, the system detects and repairs various data quality problems in big data. We demonstrate the following features of CleanCloud: (a) the support for cleaning multiple data quality problems in big data; (b) a visual tool for watching the status of big data cleaning process and tuning the parameters for data cleaning; (c) the friendly interface for data input and setting as well as cleaned data collection for big data. CleanCloud is a promising system that provides scalable and effect data cleaning mechanism for big data in either files or databases.