OpenRaiser/Intern-Atlas huggingface dataset:
From Royals to Vegans: Characterizing Question Trolling on a Community Question Answering Website
Authors: Ido Guy, Bracha Shapira
Venue: SIGIR
Year: 2018

The phenomenon of trolling has emerged as a widespread form of abuse on news sites, online social networks, and other types of social media. In this paper, we study a particular type of trolling, performed by asking a provocative question on a community question-answering website. By combining user reports with subsequent moderator deletions, we identify a set of over 400,000 troll questions on Yahoo Answers, i.e., questions aimed to inflame, upset, and draw attention from others on the community. This set of troll questions spans a lengthy period of time and a diverse set of topical categories. Our analysis reveals unique characteristics of troll questions when compared to "regular" questions, with regards to their metadata, text, and askers. A classifier built upon these features reaches an accuracy of 85% over a balanced dataset. The answers' text and metadata, reflecting the community's response to the question, are found particularly productive for the classification task.
Sover! Social Media Observer
Authors: Asmelash Teka Hadgu, Sallam Abualhaija, Claudia Niederée
Venue: SIGIR
Year: 2018

The observation of social media provides an important complementing source of information about an unfolding event such as a crisis situation. For this purpose we have developed and demonstrate Sover!, a system to monitor real-time dynamic events via Twitter targeting the needs of aid organizations. At its core it builds upon an effective adaptive crawler, which combines two social media streams in a Bayesian inference framework and after each time-window updates the probabilities of whether given keywords are relevant for an event. Sover! also exposes the crawling functionality so a user can actively influence the evolving selection of keywords. The crawling activity feeds a rich dashboard, which enables the user to get a better understanding of a crisis situation as it unfolds in real-time.
RecAdvisor: Criteria-based Ph.D. Supervisor Recommendation
Authors: Mir Anamul Hasan, Daniel G. Schwartz
Venue: SIGIR
Year: 2018

This demo presents RecAdvisor, a prototype recommender system for finding and recommending potential Ph.D. supervisors for students by identifying different criteria to consider when selecting a supervisor.
The City Brain: Towards Real-Time Search for the Real-World
Authors: Xian-Sheng Hua
Venue: SIGIR
Year: 2018

A city is an aggregate of a huge amount of heterogeneous data. However, extracting meaningful values from that data remains challenging. City Brain is an end-to-end system whose goal is to glean irreplaceable values from big city data, specifically from videos, with the assistance of rapidly evolving AI technologies and fast-growing computing capacity. From cognition to optimization, to decision-making, from search to prediction and ultimately, to intervention, City Brain improves the way we manage the city, as well as the way we live in it. In this talk, firstly we will introduce current practices of the City Brain platform in a few cities in China, including what we can do to achieve the goal and make it a reality. Then we will focus on visual search technologies and applications that we can apply on the city data. Last, a few video demos will be shown, followed by highlighting a few future directions of city computing.
Automated Comparative Table Generation for Facilitating Human Intervention in Multi-Entity Resolution
Authors: Jiacheng Huang, Wei Hu, Haoxuan Li, Yuzhong Qu
Venue: SIGIR
Year: 2018

Entity resolution (ER), the process of identifying entities that refer to the same real-world object, has long been studied in the knowledge graph (KG) community, among many others. Humans, as a valuable source of background knowledge, are increasingly getting involved in this loop by crowdsourcing and active learning, where presenting condensed and easily-compared information is vital to help human intervene in an ER task. However, current methods for single entity or pairwise summarization cannot well support humans to observe and compare multiple entities simultaneously, which impairs the efficiency and accuracy of human intervention. In this paper, we propose an automated approach to select a few important properties and values for a set of entities, and assemble them by a comparative table. We formulate several optimization problems for generating an optimal comparative table according to intuitive goodness measures and various constraints. Our experiments on real-world datasets, comparison with related work and user study demonstrate the superior efficiency, precision and user satisfaction of our approach in multi-entity resolution (MER).
Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks
Authors: Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, Edward Y. Chang
Venue: SIGIR
Year: 2018

With the revival of neural networks, many studies try to adapt powerful sequential neural models, ıe Recurrent Neural Networks (RNN), to sequential recommendation. RNN-based networks encode historical interaction records into a hidden state vector. Although the state vector is able to encode sequential dependency, it still has limited representation power in capturing complicated user preference. It is difficult to capture fine-grained user preference from the interaction sequence. Furthermore, the latent vector representation is usually hard to understand and explain. To address these issues, in this paper, we propose a novel knowledge enhanced sequential recommender. Our model integrates the RNN-based networks with Key-Value Memory Network (KV-MN). We further incorporate knowledge base (KB) information to enhance the semantic representation of KV-MN. RNN-based models are good at capturing sequential user preference, while knowledge-enhanced KV-MNs are good at capturing attribute-level user preference. By using a hybrid of RNNs and KV-MNs, it is expected to be endowed with both benefits from these two components. The sequential preference representation together with the attribute-level preference representation are combined as the final representation of user preference. With the incorporation of KB information, our model is also highly interpretable. To our knowledge, it is the first time that sequential recommender is integrated with external memories by leveraging large-scale KB information.
Towards Distributed Pairwise Ranking using Implicit Feedback
Authors: Mohsan Jameel, Nicolas Schilling, Lars Schmidt-Thieme
Venue: SIGIR
Year: 2018

Learning with pairwise ranking methods for implicit feedback datasets has shown promising results as compared to pointwise ranking methods for recommendation tasks. However, there is limited effort in scaling the pairwise ranking methods in a large scale distributed setting. In this paper we address the scalability aspect of a pairwise ranking method using Factorization Machines in distributed settings. Our proposed method is based on a block partitioning of the model parameters so that each distributed worker runs stochastic gradient updates on an independent block. We developed a dynamic block creation and exchange strategy by utilizing the frequency of occurrence of a feature in the local training data of a worker. Empirical evidence on publicly available benchmark datasets indicates that the proposed method scales better than the static block based methods and outperforms competing state-of-the-art methods.
Salton Award Keynote: Information Interaction in Context
Authors: Kalervo Järvelin
Venue: SIGIR
Year: 2018

keynote Share on Salton Award Keynote: Information Interaction in Context Author: Kalervo P. Jarvelin Univerity of Tampere, Tampere, Finland Univerity of Tampere, Tampere, FinlandView Profile Authors Info & Claims SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information RetrievalJune 2018 Pages 1–2https://doi.org/10.1145/3209978.3210230Published:27 June 2018Publication History 0citation451DownloadsMetricsTotal Citations0Total Downloads451Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Privacy-aware Ranking with Tree Ensembles on the Cloud
Authors: Shiyu Ji, Jinjin Shao, Daniel Agun, Tao Yang
Venue: SIGIR
Year: 2018

Tree-based ensembles are widely used for document ranking but supporting such a method efficiently under a privacy-preserving constraint on the cloud is an open research problem. The main challenge is that letting the cloud server perform ranking computation may unsafely reveal privacy-sensitive information. To address privacy with tree-based server-side ranking, this paper proposes to reduce the learning-to-rank model dependence on composite features as a trade-off, and develops comparison-preserving mapping to hide feature values and tree thresholds. To justify the above approach, the presented analysis shows that a decision tree with simplifiable composite features can be transformed into another tree using raw features without increasing the training accuracy loss. This paper analyzes the privacy properties of the proposed scheme, and compares the relevance of gradient boosting regression trees, LambdaMART, and random forests using raw features for several test data sets under the privacy consideration, and assesses the competitiveness of a hybrid model based on these algorithms.
A New Term Frequency Normalization Model for Probabilistic Information Retrieval
Authors: Fanghong Jian, Jimmy Xiangji Huang, Jiashu Zhao, Tingting He
Venue: SIGIR
Year: 2018

In probabilistic BM25, term frequency normalization is one of the key components. It is often controlled by parameters $k_1$ and b , which need to be optimized for each given data set. In this paper, we assume and show empirically that term frequency normalization should be specific with query length in order to optimize retrieval performance. Following this intuition, we first propose a new term frequency normalization with query length for probabilistic information retrieval, namely \textttBM25\tiny QL . Then \textttBM25\tiny QL is incorporated into the state-of-the-art models CRTER riptsize 2 and LDA-BM25, denoted as $\textttCRTER riptsize 2 ^\texttt\tiny QL $ and \textttLDA-BM25\tiny QL respectively. A series of experiments show that our proposed approaches \textttBM25\tiny QL , $\textttCRTER riptsize 2 ^\texttt\tiny QL $ and \textttLDA-BM25\tiny QL are comparable to BM25, CRTER riptsize 2 and LDA-BM25 with the optimal b setting in terms of MAP on all the data sets.
Split-Lists and Initial Thresholds for WAND-based Search
Authors: Andrew Kane, Frank Wm. Tompa
Venue: SIGIR
Year: 2018

We examine search engine performance for rank-safe query execution using the WAND and state-of-the-art BMW algorithms. Supported by extensive experiments, we suggest two approaches to improve query performance: initial list thresholds should be used when k values are large, and our split-list WAND approach should be used instead of the normal WAND or BMW approaches. We also recommend that reranking-based distributed systems use smaller k values when selecting the results to return from each partition.
A2A: Benchmark Your Clinical Decision Support Search
Authors: Sarvnaz Karimi, Vincent Nguyen, Falk Scholer, Brian Jin, Sara Falamaki
Venue: SIGIR
Year: 2018

Clinical Decision Support (CDS) systems aim to assist clinicians in their daily decision-making related to diagnosis, tests, and treatments of patients by providing relevant evidence from the scientific literature. This promise however is yet to be fulfilled, with search for relevant literature for a given patient condition still being an active research topic. The TREC CDS track was designed to address this research gap. We developed a platform to facilitate experimentation and hypothesis testing for information retrieval researchers working on this topic. It provides a large range of query and document processing techniques that are explored in the biomedical search domain.
Fine-Grained Information Identification in Health Related Posts
Authors: Hamed Khanpour, Cornelia Caragea
Venue: SIGIR
Year: 2018

Online health communities have become a medium for patients to share their personal experiences and interact with peers on topics related to a disease, medication, side effects, and therapeutic processes. Analyzing informational posts in these communities can provide an insightful view about the dominant health issues and can help patients find the information that they need easier. In this paper, we propose a computational model that mines user content in online health communities to detect positive experiences and suggestions on health improvement as well as negative impacts or side effects that cause suffering throughout fighting with a disease. Specifically, we combine high-level, abstract features extracted from a convolutional neural network with lexicon-based features and features extracted from a long short term memory network to capture the semantics in the data. We show that our model, with and without lexicon-based features, outperforms strong baselines.
Causal Inference over Longitudinal Data to Support Expectation Exploration
Authors: Emre Kiciman
Venue: SIGIR
Year: 2018

Many people use web search engines for expectation exploration: exploring what might happen if they take some action, or how they should expect some situation to evolve. While search engines have databases to provide structured answers to many questions, there is no database about the outcomes of actions or the evolution of situations. The information we need to answer such questions, however, is already being recorded. On social media, for example, hundreds of millions of people are publicly reporting about the actions they take and the situations they are in, and an increasing range of events and activities experienced in their lives over time. In this presentation, we show how causal inference methods can be applied to such individual-level, longitudinal records to generate answers for expectation exploration queries.
Design and Evaluation of Query Auto Completion Mechanisms
Authors: Unni Krishnan
Venue: SIGIR
Year: 2018

Query Auto Completion (QAC) systems present a list of completions to a partial query entered by the user. The completions extend the partial query and are updated with subsequent modifications made to the partial query. The partial query, or more generally, the query pattern is matched against a target string collection to retrieve a candidate set of strings. A scoring function is applied to the strings in the candidate set to choose a subset of them which are then presented in a specific order as the final completions. Krishnan et al. [8] define a set of QAC modes based on how a query pattern is matched with the target string collection. Each QAC mode produces distinct candidate sets and results in different completions being presented. A taxonomy of QAC modes will help us better understand the effects of match functions, desirable characteristics of an effective QAC method and their applicability to specific scenarios. The taxonomy proposed by Krishnan et al. [8] can be further refined to multiple dimensions such as error tolerance, phrase completions (without considering the order of the tokens), and completions on sub-tokens (complete just the final token vs complete each token). From an implementation aspect, each QAC mode needs specific algorithmic strategies and index structures. We measure the computational efficiency of different implementation strategies in terms of index memory, index construction time, querying and update time. For instance, a trie [1, 6, 7] is a commonly used data structure for prefix lookup, denoted “Mode 1". A different querying mode suggested by Bast andWeber [2] splits the pattern P based on white spaces and presents completions containing tokens having a prefix match with each query tokens. We call this Mode 2. Typical implementations for this mode (or its variants) use an inverted index [2, 5] to map the tokens to the documents they appear in and computes document list intersections for tokens from query pattern. If a pattern match is performed on the tokens instead of prefix match (as in Mode 2), we get “Mode 3”. A suffix tree or suffix
Crowd vs. Expert: What Can Relevance Judgment Rationales Teach Us About Assessor Disagreement?
Authors: Mücahid Kutlu, Tyler McDonnell, Yassmine Barkallah, Tamer Elsayed, Matthew Lease
Venue: SIGIR
Year: 2018

While crowdsourcing offers a low-cost, scalable way to collect relevance judgments, lack of transparency with remote crowd work has limited understanding about the quality of collected judgments. In prior work, we showed a variety of benefits from asking crowd workers to provide \em rationales for each relevance judgment \citemcdonnell2016relevant. In this work, we scale up our rationale-based judging design to assess its reliability on the 2014 TREC Web Track, collecting roughly 25K crowd judgments for 5K document-topic pairs. We also study having crowd judges perform topic-focused judging, rather than across topics, finding this improves quality. Overall, we show that crowd judgments can be used to reliably rank IR systems for evaluation. We further explore the potential of rationales to shed new light on reasons for judging disagreement between experts and crowd workers. Our qualitative and quantitative analysis distinguishes subjective vs.\ objective forms of disagreement, as well as the relative importance of each disagreement cause, and we present a new taxonomy for organizing the different types of disagreement we observe. We show that many crowd disagreements seem valid and plausible, with disagreement in many cases due to judging errors by the original TREC assessors. We also share our WebCrowd25k dataset, including: (1) crowd judgments with rationales, and (2) taxonomy category labels for each judging disagreement analyzed.
SAAN: A Sentiment-Aware Attention Network for Sentiment Analysis
Authors: Zeyang Lei, Yujiu Yang, Min Yang
Venue: SIGIR
Year: 2018

Analyzing public opinions towards products, services and social events is an important but challenging task. Despite the remarkable successes of deep neural networks in sentiment analysis, these approaches do not make full use of the prior sentiment knowledge (e.g., sentiment lexicon, negation words, intensity words). In this paper, we propose a Sentiment-Aware Attention Network (SAAN) to boost the performance of sentiment analysis, which adopts a three-step strategy to learn the sentiment-specific sentence representation. First, we employ a word-level mutual attention mechanism to model word-level correlation. Next, a phrase-level convolutional attention is designed to obtain phrase-level correlation. Finally, a sentence-level multi-head attention mechanism is proposed to capture various sentimental information from different subspaces. The experiments on Movie Review (MR) and Stanford Sentiment Treebank (SST) show that our model consistently outperform the previous methods for sentiment analysis.
API Caveat Explorer - Surfacing Negative Usages from Practice: An API-oriented Interactive Exploratory Search System for Programmers
Authors: Jing Li, Aixin Sun, Zhenchang Xing, Lei Han
Venue: SIGIR
Year: 2018

Application programming interface (API) documentation well describes an API and how to use it. However, official documentation does not describe "how not to use it" or the different kinds of errors when an API is used wrongly. Programming caveats are negative usages of an API. When these caveats are overlooked, errors may emerge, leading to heavy discussions on Q&A websites like Stack Overflow. In this demonstration, we present API Caveat Explorer, a search system to explore API caveats that are mined from large-scale unstructured discussions on Stack Overflow. API Caveat Explorer takes API-oriented queries such as "HashMap" and retrieves API caveats by text summarization techniques. API caveats are represented by sentences, which are context-independent, prominent, semantically diverse and non-redundant. The system provides a web-based interface that allows users to interactively explore the full picture of all discovered caveats of an API, and the details of each. The potential users of API Caveat Explorer are programmers and educators for learning and teaching APIs.
Top-k Route Search through Submodularity Modeling of Recurrent POI Features
Authors: Hongwei Liang, Ke Wang
Venue: SIGIR
Year: 2018

We consider a practical top-k route search problem: given a collection of points of interest (POIs) with rated features and traveling costs between POIs, a user wants to find k routes from a source to a destination and limited in a cost budget, that maximally match her needs on feature preferences. One challenge is dealing with the personalized diversity requirement where users have various trade-off between quantity (the number of POIs with a specified feature) and variety (the coverage of specified features). Another challenge is the large scale of the POI map and the great many alternative routes to search. We model the personalized diversity requirement by the whole class of submodular functions, and present an optimal solution to the top-k route search problem through indices for retrieving relevant POIs in both feature and route spaces and various strategies for pruning the search space using user preferences and constraints. We also present promising heuristic solutions and evaluate all the solutions on real life data.
Learning to Detect Pathogenic Microorganism of Community-acquired Pneumonia
Authors: Wenwei Liang, Wei Zhang, Bo Jin, Jiangjiang Xu, Linhua Shu, Hongyuan Zha
Venue: SIGIR
Year: 2018

Community-acquired pneumonia (CAP) is a major death cause for children, requiring an early administration of appropriate antibiotics to cure it. To achieve this, accurate detection of pathogenic microorganism is crucial, especially for reducing the abuse of antibiotics. Conventional gold standard detection methods are mainly etiology based, incurring high cost and labor intensity. Although recently electronic health records (EHRs) become prevalent and widely used, their power for automatically determining pathogenic microorganism has not been investigated. In this paper, we formulate a new problem for automatically detecting pathogenic microorganism of CAP by considering patient biomedical features from EHRs, including time-varying body temperatures and common laboratory measurements. We further develop a Patient Attention based Recurrent Neural Network (PA-RNN) model to fuse different patient features for detection. We conduct experiments on a real dataset, demonstrating utilizing electronic health records yields promising performance and PA-RNN outperforms several alternatives.