OpenRaiser/Intern-Atlas huggingface dataset:
Ranking Rich Mobile Verticals based on Clicks and Abandonment
Authors: Mami Kawasaki, Inho Kang, Tetsuya Sakai
Venue: CIKM
Year: 2017

We consider the problem of ranking rich verticals, which we call "cards," for a given mobile search query. Examples of card types include "SHOP" (showing access and contact information of a shop), "WEATHER" (showing a weather forecast for a particular location), and "TV" (showing information about a TV programme). These cards can be highly visual and/or concise, and may often satisfy the user's information need without making her click on them. While this "good abandonment" of the search engine result page is ideal especially for mobile environments where the interaction between the user and the search engine should be minimal, it poses a challenge for search engine companies whose ranking algorithms rely heavily on click data. In order to provide the right card types to the user for a given query, we propose a graph-based approach which extends a click-based automatic relevance estimation algorithm of Agrawal et al., by incorporating an abandonment-based preference rule. Using a real mobile query log from a commercial search engine, we constructed a data set containing 2,472 pairwise card type preferences covering 992 distinct queries, by hiring three independent assessors. Our proposed method outperforms a click-only baseline by 53-68% in terms of card type preference accuracy. The improvement is also statistically highly significant, with p ≈ 0.0000 according to the paired randomisation test.
Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary
Authors: Krishnaram Kenthapadi, Stuart Ambler, Liang Zhang, Deepak Agarwal
Venue: CIKM
Year: 2017

The recently launched LinkedIn Salary product has been designed with the goal of providing compensation insights to the world's professionals and thereby helping them optimize their earning potential. We describe the overall design and architecture of the statistical modeling system underlying this product. We focus on the unique data mining challenges while designing and implementing the system, and describe the modeling components such as Bayesian hierarchical smoothing that help to compute and present robust compensation insights to users. We report on extensive evaluation with nearly one year of de-identified compensation data collected from over one million LinkedIn users, thereby demonstrating the efficacy of the statistical models. We also highlight the lessons learned through the deployment of our system at LinkedIn.
SemFacet: Making Hard Faceted Search Easier
Authors: Evgeny Kharlamov, Luca Giacomelli, Evgeny Sherkhonov, Bernardo Cuenca Grau, Egor V. Kostylev, Ian Horrocks
Venue: CIKM
Year: 2017

Faceted search is a prominent search paradigm that became the standard in many Web applications and has also been recently proposed as a suitable paradigm for exploring and querying RDF graphs. One of the main challenges that hampers usability of faceted search systems especially in the RDF context is information overload, that is, when the size of faceted interfaces becomes comparable to the size of the data over which the search is performed. In this demo we present (an extension of) our faceted search system SemFacet and focus on features that address the information overload: ranking, aggregation, and reachability. The demo attendees will be able to try our system on an RDF graph that models online shopping over a catalogs with up to millions of products.
Semantic Rules for Machine Diagnostics: Execution and Management
Authors: Evgeny Kharlamov, Ognjen Savkovic, Guohui Xiao, Rafael Peñaloza, Gulnar Mehdi, Mikhail Roshchin, Ian Horrocks
Venue: CIKM
Year: 2017

Rule-based diagnostics of equipment is an important task in industry. In this paper we present how semantic technologies can enhance diagnostics. In particular, we present our semantic rule language sigRL that is inspired by the real diagnostic languages used in Siemens. SigRL allows to write compact yet powerful diagnostic programs by relying on a high level data independent vocabulary, diagnostic ontologies, and queries over these ontologies. We study computational complexity of SigRL: execution of diagnostic programs, provenance computation, as well as automatic verification of redundancy and inconsistency in diagnostic programs.
Smart Infrastructure Maintenance Using Incremental Tensor Analysis: Extended Abstract
Authors: Nguyen Lu Dang Khoa, Ali Anaissi, Yang Wang
Venue: CIKM
Year: 2017

Civil infrastructures are key to the flow of people and goods in urban environments. Structural Health Monitoring (SHM) is a condition-based maintenance technology, which provides and predicts actionable information on the current and future states of infrastructures. SHM data are usually multi-way data which are produced by multiple highly correlated sensors. Tensor decomposition allows the learning from such data in temporal, spatial and feature modes at the same time. However, to facilitate a real time response for online learning, incremental tensor update need to be used when new data come in, rather than doing the decomposition in a batch manner. This work proposed a method called onlineCP-ALS to incrementally update tensor component matrices, followed by a self-tuning one-class support vector machine for online damage identification. Moreover, a robust clustering technique was applied on the tensor space for online substructure grouping and anomaly detection. These methods were applied to data from lab-based structures and also data collected from the Sydney Harbour Bridge in Australia. We obtained accurate damage detection accuracies for all these datasets. Damage locations were also captured correctly, and different levels of damage severity were well estimated. Furthermore, the clustering technique was able to detect spatial anomalies, which were associated with sensor and instrumentation issues. Our proposed method was efficient and much faster than the batch approach.
Hybrid BiLSTM-Siamese network for FAQ Assistance
Authors: Prerna Khurana, Puneet Agarwal, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan
Venue: CIKM
Year: 2017

We describe an automated assistant for answering frequently asked questions; our system has been deployed, and is currently answering HR-related queries in two different areas (leave management and health insurance) to a large number of users. The needs of a large global corporate lead us to model a frequently asked question (FAQ) to be an equivalence class of actually asked questions, for which there is a common answer (certified as being consistent with the organization's policy). When a new question is posed to our system, it finds the class of question, and responds with the answer for the class. At this point, the system is either correct (gives correct answer); or incorrect (gives wrong answer); or incomplete (says "I don't know''). We employ a hybrid deep-learning architecture in which a BiLSTM-based classifier is combined with second BiLSTM-based Siamese network in an iterative manner: Questions for which the classifier makes an error during training are used to generate a set of misclassified question-question pairs. These, along with correct pairs, are used to train the Siamese network to drive apart the (hidden) representations of the misclassified pairs. We present experimental results from our deployment showing that our iteratively trained hybrid network: (a) results in better performance than using just a classifier network, or just a Siamese network; (b) performs better than state-of-the art sentence classifiers in the two areas in which it has been deployed, in terms of both accuracy as well as precision-recall tradeoff; and (c) also performs well on a benchmark public dataset. We also observe that using question-question pairs in our hybrid network, results in marginally better performance than using question-to-answer pairs. Finally, estimates of precision and recall from the deployment of our automated assistant suggest that we can expect the burden on our HR department to drop from answering about 6000 queries a day to about 1000.
DiagTree: Diagnostic Tree for Differential Diagnosis
Authors: Yejin Kim, Jingyun Choi, Yosep Chong, Xiaoqian Jiang, Hwanjo Yu
Venue: CIKM
Year: 2017

Differential diagnosis is detection of one disease among similar diseases using evidence such as pathologic tests. A Partially Observed Markov Decision Process (POMDP) formulates the complex differential diagnosis process into a probabilistic decision-making model. However, differential diagnosis is not often fully formulated as POMDP because model construction does not consider the cost (or time) to finish the diagnosis process, or the practical convention on clinical tests. We propose a Diagnostic Tree (DiagTree), a new framework for diagnosing diseases, which combines several tests to reduce the diagnosis time and to incorporate real-world constraints into discrete optimization. DiagTree consists of multiple tests in internal nodes and posterior probabilities ("confidences") that the patient suffers the disease listed at each leaf node. The confidences are computed after a series of test results is applied in internal nodes. DiagTree is built to maximize the confidences at leaf nodes and to minimize the decision process time. We formulate this problem as integer programming and solve it by the Branch-and-Bound method and a greedy approach. We apply DiagTree to immunohistochemistry profiles to detect lymphoid neoplasms. We evaluate the accuracy and cost of the diagnosis rules from DiagTree compared to those obtained using rules that clinicians derived from their experience. DiagTree detected diseases with high accuracy and also reduced the diagnosis cost (or time) compared to the existing rules of clinicians. DiagTree can support clinicians by suggesting a simple diagnosis process with high accuracy and low cost among test candidates.
Construction of a National Scale ENF Map using Online Multimedia Data
Authors: Hyunsoo Kim, Youngbae Jeon, Ji Won Yoon
Venue: CIKM
Year: 2017

The frequency of power distribution networks in a power grid is called electrical network frequency (ENF). Because it provides the spatio-temporal changes of the power grid in a particular location, ENF is used in many application domains including the prediction of grid instability and blackouts, detection of system breakup, and even digital forensics. In order to build high performing applications and systems, it is necessary to capture a large-scale nationwide or worldwide ENF map. Consequently, many studies have been conducted on the distribution of specialized physical devices that capture the ENF signals. However, this approach is not practical because it requires significant effort from design to setup, moreover, it has a limitation in its efficiency to monitor and stably retain the collection equipment distributed throughout the world. Furthermore, this approach requires a significant budget.
Modeling Affinity based Popularity Dynamics
Authors: Minkyoung Kim, Daniel A. McFarland, Jure Leskovec
Venue: CIKM
Year: 2017

Information items draw collective attention across a heterogeneous social system, leading to great disparities of popularity. Unveiling underlying diffusion processes is very challenging, since a social system consists of time-evolving subgroups interacting and exerting disproportionate influences on an individual item's popularity. In this study, we propose the Affinity Poisson Process model (APP) which models popularity dynamics, by incorporating (1) affinities between subgroups, (2) heterogeneous preferential attachment, and (3) subgroup-level time decay. As a case study, we apply our proposed model to scholarly publications in computer science. Our model outperforms the state of the art approach in predicting citation volumes of individual papers. More importantly, the proposed model enables us to uncover popularity dynamics driven by intra- and inter-subgroup interactions, which has been neglected in prior work. We expect that our model can afford interpretable insights on the attention economy in terms of affinity and aging effect.
Machine Learning based Performance Modeling of Flash SSDs
Authors: Jaehyung Kim, Jinuk Park, Sanghyun Park
Venue: CIKM
Year: 2017

Flash memory based solid state drives(SSDs) have alleviated the I/O bottleneck by exploiting its data parallel design. In an enterprise environment, Flash SSD used in the form of a hybrid storage architecture to achieve the better performance with lower cost. In this architecture, I/O load balancing is one of the important factors. However, the internal parallelism distorts the performance measures of the flash SSDs. Despite the criticality of load balancing on I/O intensive environments, these studies have rarely been addressed. In this paper, we examine the effectiveness of applying classification method using machine learning techniques to the I/O saturation estimation by using Linux kernel I/O statistics instead of the utilization measure that is currently used for HDDs. We conclude that machine learning techniques that we employed (Support Vector Machine and LASSO Generalized Linear Model) performs well compared to the existing utilization measure even we cannot collect the internal information of the flash SSDs.
GPU-Accelerated Graph Clustering via Parallel Label Propagation
Authors: Yusuke Kozawa, Toshiyuki Amagasa, Hiroyuki Kitagawa
Venue: CIKM
Year: 2017

Graph clustering has recently attracted much attention as a technique to extract community structures from various kinds of graph data. Since available graph data becomes increasingly large, the acceleration of graph clustering is an important issue for handling large-scale graphs. To this end, this paper proposes a fast graph clustering method using GPUs. The proposed method is based on parallelization of label propagation, one of the fastest graph clustering algorithms. Our method has the following three characteristics: (1) efficient parallelization: the algorithm of label propagation is transformed into a sequence of data-parallel primitives; (2) load balance: the method takes into account load balancing by adopting the primitives that make the load among threads and blocks well balanced; and (3) out-of-core processing: we also develop algorithms to efficiently deal with large-scale datasets that do not fit into GPU memory. Moreover, this GPU out-of-core algorithm is extended to simultaneously exploit both CPUs and GPUs for further performance gain. Extensive experiments with real-world and synthetic datasets show that our proposed method outperforms an existing parallel CPU implementation by a factor of up to 14.3 without sacrificing accuracy.
A Hyper-connected World
Authors: K. Ananth Krishnan
Venue: CIKM
Year: 2017

As the world gets hyper-connected, cities are evolving into complex ecosystems, technically and behaviourally. Machines and humans interact continually, generating streams of data and behavior patterns. To be a true smart city in a hyper-connected world, cities today have to use technology like a modern enterprise: build a digital spine; become intelligent and leverage automation. However, this technology core should be people centric. In a multiple stakeholder ecosystem, city administrators, industries and citizens, will look at the city from a different perspective and expect different experiences. Finally citizen experience will be the determinant of success of a smart city. While articulating this vision, Ananth will highlight how differently businesses must orient themselves in this environment.
Unsupervised Concept Categorization and Extraction from Scientific Document Titles
Authors: Adit Krishnan, Aravind Sankar, Shi Zhi, Jiawei Han
Venue: CIKM
Year: 2017

This paper studies the automated categorization and extraction of scientific concepts from titles of scientific articles, in order to gain a deeper understanding of their key contributions and facilitate the construction of a generic academic knowledgebase. Towards this goal, we propose an unsupervised, domain-independent, and scalable two-phase algorithm to type and extract key concept mentions into aspects of interest (e.g., Techniques, Applications, etc.). In the first phase of our algorithm we proposePhraseType, a probabilistic generative model which exploits textual features and limited POS tags to broadly segment text snippets into aspect-typed phrases. We extend this model to simultaneously learn aspect-specific features and identify academic domains in multi-domain corpora, since the two tasks mutually enhance each other. In the second phase, we propose an approach based on adaptor grammars to extract fine grained concept mentions from the aspect-typed phrases without the need for any external resources or human effort, in a purely data-driven manner. We apply our technique to study literature from diverse scientific domains and show significant gains over state-of-the-art concept extraction techniques. We also present a qualitative analysis of the results obtained.
A Robust Named-Entity Recognition System Using Syllable Bigram Embedding with Eojeol Prefix Information
Authors: Sunjae Kwon, Youngjoong Ko, Jungyun Seo
Venue: CIKM
Year: 2017

Korean named-entity recognition (NER) systems have been developed mainly on the morphological-level, and they are commonly based on a pipeline framework that identifies named-entities (NEs) following the morphological analysis. However, this framework can mean that the performance of NER systems is degraded, because errors from the morphological analysis propagate into NER systems. This paper proposes a novel syllable-level NER system, which does not require a morphological analysis and can achieve a similar or better performance compared with the morphological-level NER systems. In addition, because the proposed system does not require a morphological analysis step, its processing speed is about 1.9 times faster than those of the previous morphological-level NER systems.
Indexable Bayesian Personalized Ranking for Efficient Top-k Recommendation
Authors: Dung D. Le, Hady W. Lauw
Venue: CIKM
Year: 2017

Top-k recommendation seeks to deliver a personalized recommendation list of k items to a user. The dual objectives are (1) accuracy in identifying the items a user is likely to prefer, and (2) efficiency in constructing the recommendation list in real time. One direction towards retrieval efficiency is to formulate retrieval as approximate k nearest neighbor (kNN) search aided by indexing schemes, such as locality-sensitive hashing, spatial trees, and inverted index. These schemes, applied on the output representations of recommendation algorithms, speed up the retrieval process by automatically discarding a large number of potentially irrelevant items when given a user query vector. However, many previous recommendation algorithms produce representations that may not necessarily align well with the structural properties of these indexing schemes, eventually resulting in a significant loss of accuracy post-indexing. In this paper, we introduce Indexable Bayesian Personalized Ranking (IBPR) that learns from ordinal preference to produce representation that is inherently compatible with the aforesaid indices. Experiments on publicly available datasets show superior performance of the proposed model compared to state-of-the-art methods on top-k recommendation retrieval task, achieving significant speedup while maintaining high accuracy.
SemVis: Semantic Visualization for Interactive Topical Analysis
Authors: Tuan M. V. Le, Hady W. Lauw
Venue: CIKM
Year: 2017

Exploratory analysis of a text corpus is an important task that can be aided by informative visualization. One spatially-oriented form of document visualization is a scatterplot, whereby every document is associated with a coordinate, and relationships among documents can be perceived through their spatial distances. Semantic visualization further infuses the visualization space with latent semantics, by incorporating a topic model that has a representation in the visualization space, allowing users to also perceive relationships between documents and topics spatially. We illustrate how a semantic visualization system called SemVis could be used to navigate a text corpus interactively and topically via browsing and searching.
Exploring the Veracity of Online Claims with BackDrop
Authors: Julien Leblay, Weiling Chen, Steven J. Lynden
Venue: CIKM
Year: 2017

Using the Web to assess the validity of claims presents many challenges. Whether the data comes from social networks or established media outlets, individual or institutional data publishers, one has to deal with scale and heterogeneity, as well as with incomplete, imprecise and sometimes outright false information. All of these are closely studied issues. Yet in many situations, the claims under scrutiny, and the data itself, have some inherent context-dependency making them impossible to completely disprove, or evaluate through a simple (e.g. scalar) measure. While data models used on the Web typically deal with universal knowledge, we believe the time has come to put context, such as time or provenance, at the forefront and watch knowledge through multiple lenses. We present BackDrop, an application that enables annotating knowledge and ontologies found online to explore how the veracity of claims varies with context. BackDrop comes in the form of a Web interface, in which users can interactively populate and annotate knowledge bases, and explore under which circumstances certain claims are more or less credible.
Computing Betweenness Centrality in B-hypergraphs
Authors: Kwang Hee Lee, Myoung-Ho Kim
Venue: CIKM
Year: 2017

The directed hypergraph (especially B-hypergraph) has hyperedges that represent relations of a set of source nodes to a single target node. Author-cited networks and cellular signaling pathways can be modeled as a B-hypergraph. In this paper every source node of a hyperedge in the shortest path p in a B-hypergraph is considered a participant of p. We propose a betweenness centrality in the B-hypergraph that measures the number of shortest paths in which a node participates. The algorithm for computing the approximated betweenness centrality scores is also proposed. Through various performance experiments such as attack robustness and reachability tests, we show that our proposed betweenness centrality is a more appropriate measure in real-world B-hypergraph applications than ordinary betweenness centrality.
IDAE: Imputation-boosted Denoising Autoencoder for Collaborative Filtering
Authors: Jae-woong Lee, Jongwuk Lee
Venue: CIKM
Year: 2017

In recent years, while deep neural networks have shown impressive performance to solve various recognition and classification problems, collaborative filtering (CF) received relatively little attention to utilize deep neural networks. Because of inherent data sparsity, it remains a challenging problem for deep neural networks. In this paper, we propose a new CF model, namely the imputation-boosted denoising autoencoder (IDAE), for top-N recommendation. Specifically, IDAE consists of two steps: imputing positive values and learning with imputed values. First, it infers and imputes positive user feedback from missing values. Then, the correlation between items is learned by using the denoising autoencoder (DAE) with imputed values. Unlike the existing DAE that randomly corrupts the input, the key characteristic of IDAE is that original user values are taken as the input, and imputed values are reflected as the corrupted output. Our experimental results demonstrate that IDAE significantly outperforms state-of-the-art CF algorithms using autoencoders (by up to 5%) on the MovieLens datasets.