OpenRaiser/Intern-Atlas huggingface dataset:
SEQ: Example-based Query for Spatial Objects
Authors: Siqiang Luo, Jiafeng Hu, Reynold Cheng, Jing Yan, Ben Kao
Venue: CIKM
Year: 2017

Spatial object search is prevalent in map services (e.g., Google Maps). To rent an apartment, for example, one will take into account its nearby facilities, such as supermarkets, hospitals, and subway stations. Traditional keyword search solutions, such as the nearby function in Google Maps, are insufficient in expressing the often complex attribute/spatial requirements of users. Those require- ments, however, are essential to reflect the user search intention. In this paper, we propose the Spatial Exemplar Query (SEQ), which allows the user to input a result example over an interface inside the map service. We then propose an effective similarity measure to evaluate the proximity between a candidate answer and the given example. We conduct a user study to validate the effectiveness of SEQ. Our result shows that more than 88% of users would like to have an example assisted search in map services. Moreover, SEQ gets a user satisfactory score of 4.3/5.0, which is more than 2 times higher than that of a baseline solution.
Privacy-Preserving Collaborative Deep Learning with Application to Human Activity Recognition
Authors: Lingjuan Lyu, Xuanli He, Yee Wei Law, Marimuthu Palaniswami
Venue: CIKM
Year: 2017

The proliferation of wearable devices has contributed to the emergence of mobile crowdsensing, which leverages the power of the crowd to collect and report data to a third party for large-scale sensing and collaborative learning. However, since the third party may not be honest, privacy poses a major concern. In this paper, we address this concern with a two-stage privacy-preserving scheme called RG-RP: the first stage is designed to mitigate maximum a posteriori (MAP) estimation attacks by perturbing each participant's data through a nonlinear function called repeated Gompertz (RG); while the second stage aims to maintain accuracy and reduce transmission energy by projecting high-dimensional data to a lower dimension, using a row-orthogonal random projection (RP) matrix. The proposed RG-RP scheme delivers better recovery resistance to MAP estimation attacks than most state-of-the-art techniques on both synthetic and real-world datasets. For collaborative learning, we proposed a novel LSTM-CNN model combining the merits of Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). Our experiments on two representative movement datasets captured by wearable sensors demonstrate that the proposed LSTM-CNN model outperforms standalone LSTM, CNN and Deep Belief Network. Together, RG+RP and LSTM-CNN provide a privacy-preserving collaborative learning framework that is both accurate and privacy-preserving.
Truth Discovery by Claim and Source Embedding
Authors: Shanshan Lyu, Wentao Ouyang, Huawei Shen, Xueqi Cheng
Venue: CIKM
Year: 2017

Information gathered from multiple sources on the Web often exhibits conflicts. This phenomenon motivates the need of truth discovery, which aims to automatically find the true claim among multiple conflicting claims. Existing truth discovery methods are mainly based on iterative updates or probabilistic models. In particular, iterative methods specify rules that govern how credibility flows from sources to claims and then back to sources. However, these manually-defined rules tend to be ad hoc and are difficult to adapt and analyze. Probabilistic methods model a few latent factors that impact how sources make claims, such as randomly choosing, guessing, or mistaking. However, these manually-defined factors may not well reflect the underlying data distributions. Given these limitations, we propose a new, unsupervised model for truth discovery in this paper. Our model first constructs a heterogenous network that exploits both source-claim and source-source relationships. It then embeds the network into a low dimensional space through a principled algorithm such that trustworthy sources and true claims (meanwhile, unreliable sources and false claims) are close. In this way, truth discovery can be conveniently performed in the embedding space. Compared with existing methods, our model does not need manually-defined rules or factors. Rather, it learns the embeddings automatically from data. Experiments on two real-world datasets demonstrate that our model outperforms existing state-of-the-art methods for truth discovery.
Enhancing the Network Embedding Quality with Structural Similarity
Authors: Tianshu Lyu, Yuan Zhang, Yan Zhang
Venue: CIKM
Year: 2017

Neural network techniques are widely used in network embedding, boosting the result of node classification, link prediction, visualization and other tasks in both aspects of efficiency and quality. All the state of art algorithms put effort on the neighborhood information and try to make full use of it. However, it is hard to recognize core periphery structures simply based on neighborhood. In this paper, we first discuss the influence brought by random-walk based sampling strategies to the embedding results. Theoretical and experimental evidences show that random-walk based sampling strategies fail to fully capture structural equivalence. We present a new method, SNS, that performs network embeddings using structural information (namely graphlets) to enhance its quality. SNS effectively utilizes both neighbor information and local-subgraphs similarity to learn node embeddings. This is the first framework that combines these two aspects as far as we know, positively merging two important areas in graph mining and machine learning. Moreover, we investigate what kinds of local-subgraph features matter the most on the node classification task, which enables us to further improve the embedding quality. Experiments show that our algorithm outperforms other unsupervised and semi-supervised neural network embedding algorithms on several real-world datasets.
Multi-view Clustering with Graph Embedding for Connectome Analysis
Authors: Guixiang Ma, Lifang He, Chun-Ta Lu, Weixiang Shao, Philip S. Yu, Alex D. Leow, Ann B. Ragin
Venue: CIKM
Year: 2017

Multi-view clustering has become a widely studied problem in the area of unsupervised learning. It aims to integrate multiple views by taking advantages of the consensus and complimentary information from multiple views. Most of the existing works in multi-view clustering utilize the vector-based representation for features in each view. However, in many real-world applications, instances are represented by graphs, where those vector-based models cannot fully capture the structure of the graphs from each view. To solve this problem, in this paper we propose a Multi-view Clustering framework on graph instances with Graph Embedding (MCGE). Specifically, we model the multi-view graph data as tensors and apply tensor factorization to learn the multi-view graph embeddings, thereby capturing the local structure of graphs. We build an iterative framework by incorporating multi-view graph embedding into the multi-view clustering task on graph instances, jointly performing multi-view clustering and multi-view graph embedding simultaneously. The multi-view clustering results are used for refining the multi-view graph embedding, and the updated multi-view graph embedding results further improve the multi-view clustering. Extensive experiments on two real brain network datasets (i.e., HIV and Bipolar) demonstrate the superior performance of the proposed MCGE approach in multi-view connectome analysis for clinical investigation and application.
LARM: A Lifetime Aware Regression Model for Predicting YouTube Video Popularity
Authors: Changsha Ma, Zhisheng Yan, Chang Wen Chen
Venue: CIKM
Year: 2017

Online content popularity prediction provides substantial value to a broad range of applications in the end-to-end social media systems, from network resource allocation to targeted advertising. While using historical popularity can predict the near-term popularity with a reasonable accuracy, the bursty nature of online content popularity evolution makes it difficult to capture the correlation between historical data and future data in the long term. Although various existing efforts have been made toward long-term prediction, they need to accumulate a long enough historical data before the prediction and their model assumptions cannot be applied to the complex YouTube networks with inherent unpredictability.
Beyond Success Rate: Utility as a Search Quality Metric for Online Experiments
Authors: Widad Machmouchi, Ahmed Hassan Awadallah, Imed Zitouni, Georg Buscher
Venue: CIKM
Year: 2017

User satisfaction metrics are an integral part of search engine development as they help system developers to understand and evaluate the quality of the user experience. Research to date has mostly focused on predicting success or frustration as a proxy for satisfaction. However, users' search experience is more complex than merely being either successful or not. As such, using success rate as a measure of satisfaction can be limiting. In this work, we propose the use of utility as a measure of searcher satisfaction. This concept represents the fulfillment a user receives from con-suming a service and explains how users aim to gain optimal overall satisfaction. Our utility metrics measure the user satisfac-tion by aggregating all their interaction with the search engine. These interactions are represented as a timeline of actions and their dwelltimes, where each action is classified as having a posi-tive or negative effect on the user. We examine sessions mined from Bing logs, with multi-point scale assessment of searcher satisfaction and show that utility is a better proxy for satisfaction compared to success. Leveraging that data, we design metrics of searcher satisfaction that assess the overall utility accumulated by a user during her search session. We use real user traffic from millions of users in an A/B setting to compare utility metrics to success rate metrics. We show that utility is a better metric for evaluating searcher satisfaction with the search engine, and a more sensitive and accurate metric when compared to predicting success. These metrics are currently adopted as the top-level met-ric for evaluating the thousands of A/B experiments that are run on Bing each year.
Active Network Alignment: A Matching-Based Approach
Authors: Eric Malmi, Aristides Gionis, Evimaria Terzi
Venue: CIKM
Year: 2017

Network alignment is the problem of matching the nodes of two graphs, maximizing the similarity of the matched nodes and the edges between them. This problem is encountered in a wide array of applications---from biological networks to social networks to ontologies---where multiple networked data sources need to be integrated. Due to the difficulty of the task, an accurate alignment can rarely be found without human assistance. Thus, it is of great practical importance to develop network alignment algorithms that can optimally leverage experts who are able to provide the correct alignment for a small number of nodes. Yet, only a handful of existing works address this active network alignment setting. The majority of the existing active methods focus on absolute queries ("are nodes a and b the same or not?"), whereas we argue that it is generally easier for a human expert to answer relative queries ("which node in the set b1,...,bn is the most similar to node a?"). This paper introduces two novel relative-query strategies, TopMatchings and GibbsMatchings, which can be applied on top of any network alignment method that constructs and solves a bipartite matching problem. Our methods identify the most informative nodes to query by sampling the matchings of the bipartite graph associated to the network-alignment instance. We compare the proposed approaches to several commonly-used query strategies and perform experiments on both synthetic and real-world datasets. Our sampling-based strategies yield the highest overall performance, outperforming all the baseline methods by more than 15 percentage points in some cases. In terms of accuracy, TopMatchings and GibbsMatchings perform comparably. However, GibbsMatchings is significantly more scalable, but it also requires hyperparameter tuning for a temperature parameter.
A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation
Authors: Jarana Manotumruksa, Craig Macdonald, Iadh Ounis
Venue: CIKM
Year: 2017

Recommending a ranked list of interesting venues to users based on their preferences has become a key functionality in Location-Based Social Networks (LBSNs) such as Yelp and Gowalla. Bayesian Personalised Ranking (BPR) is a popular pairwise recommendation technique that is used to generate the ranked list of venues of interest to a user, by leveraging the user's implicit feedback such as their check-ins as instances of positive feedback, while randomly sampling other venues as negative instances. To alleviate the sparsity that affects the usefulness of recommendations by BPR for users with few check-ins, various approaches have been proposed in the literature to incorporate additional sources of information such as the social links between users, the textual content of comments, as well as the geographical location of the venues. However, such approaches can only readily leverage one source of additional information for negative sampling. Instead, we propose a novel Personalised Ranking Framework with Multiple sampling Criteria (PRFMC) that leverages both geographical influence and social correlation to enhance the effectiveness of BPR. In particular, we apply a multi-centre Gaussian model and a power-law distribution method, to capture geographical influence and social correlation when sampling negative venues, respectively. Finally, we conduct comprehensive experiments using three large-scale datasets from the Yelp, Gowalla and Brightkite LBSNs. The experimental results demonstrate the effectiveness of fusing both geographical influence and social correlation in our proposed PRFMC framework and its superiority in comparison to BPR-based and other similar ranking approaches. Indeed, our PRFMC approach attains a 37% improvement in MRR over a recently proposed approach that identifies negative venues only from social links.
Learning Temporal Ambiguity in Web Search Queries
Authors: Behrooz Mansouri, Mohammad Sadegh Zahedi, Maseud Rahgozar, Farhad Oroumchian, Ricardo Campos
Venue: CIKM
Year: 2017

Time has strong influence on web search. The temporal intent of the searcher adds an important dimension to the relevance judgments of web queries. However, lack of understanding their temporal requirements increases the ambiguity of the queries, turning retrieval effectiveness improvements into a complex task. In this paper, we propose an approach to classify web queries into four different categories considering their temporal ambiguity. For each query, we develop features from its search volumes and related queries using Google trends and its related top Wikipedia pages. Our experiment results show that these features can determine temporal ambiguity of a given query with high accuracy. We have demonstrated that a Multilayer Perceptron Networks can achieve better results in classifying temporal class of queries in comparison to other classifiers.
Online Expectation-Maximization for Click Models
Authors: Ilya Markov, Alexey Borisov, Maarten de Rijke
Venue: CIKM
Year: 2017

Click models allow us to interpret user click behavior in search interactions and to remove various types of bias from user clicks. Existing studies on click models consider a static scenario where user click behavior does not change over time. We show empirically that click models deteriorate over time if retraining is avoided. We then adapt online expectation-maximization (EM) techniques to efficiently incorporate new click/skip observations into a trained click model. Our instantiation of Online EM for click models is orders of magnitude more efficient than retraining the model from scratch using standard EM, while loosing little in quality. To deal with outdated click information, we propose a variant of online EM called EM with Forgetting, which surpasses the performance of complete retraining while being as efficient as Online EM.
SemDia: Semantic Rule-Based Equipment Diagnostics Tool
Authors: Gulnar Mehdi, Evgeny Kharlamov, Ognjen Savkovic, Guohui Xiao, Elem Güzel Kalayci, Sebastian Brandt, Ian Horrocks, Mikhail Roshchin, Thomas A. Runkler
Venue: CIKM
Year: 2017

Rule-based diagnostics of power generating equipment is an important task in industry. In this demo 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 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 present our diagnostic system SemDia. The attendees will be able to write diagnostic programs in SemDia using sigRL over 50 Siemens turbines. We also present how such programs can be automatically verified for redundancy and inconsistency. Moreover, the attendees will see the provenance service that SemDia provides to trace the origin of diagnostic results.
Deep Sequential Models for Task Satisfaction Prediction
Authors: Rishabh Mehrotra, Ahmed Hassan Awadallah, Milad Shokouhi, Emine Yilmaz, Imed Zitouni, Ahmed El Kholy, Madian Khabsa
Venue: CIKM
Year: 2017

Detecting and understanding implicit signals of user satisfaction are essential for experimentation aimed at predicting searcher satisfaction. As retrieval systems have advanced, search tasks have steadily emerged as accurate units not only to capture searcher's goals but also in understanding how well a system is able to help the user achieve that goal. However, a major portion of existing work on modeling searcher satisfaction has focused on query level satisfaction. The few existing approaches for task satisfaction prediction have narrowly focused on simple tasks aimed at solving atomic information needs.
Task Embeddings: Learning Query Embeddings using Task Context
Authors: Rishabh Mehrotra, Emine Yilmaz
Venue: CIKM
Year: 2017

Continuous space word embedding have been shown to be highly effective in many information retrieval tasks. Embedding representation models make use of local information available in immediately surrounding words to project nearby context words closer in the embedding space. With rising multi-tasking nature of web search sessions, users often try to accomplish different tasks in a single search session. Consequently, the search context gets polluted with queries from different unrelated tasks which renders the context heterogeneous. In this work, we hypothesize that task information provides better context for IR systems to learn from. We propose a novel task context embedding architecture to learn representation of queries in low-dimensional space by leveraging their task context information from historical search logs using neural embedding models. In addition to qualitative analysis, we empirically demonstrate the benefit of leveraging task context to learn query representations.
Deception Detection: When Computers Become Better than Humans
Authors: Rada Mihalcea
Venue: CIKM
Year: 2017

Whether we like it or not, deception happens every day and everywhere: thousands of trials taking place daily around the world; little white lies: "I'm busy that day!" even if your calendar is blank; news "with a twist" (a.k.a. fake news) meant to attract the readers attraction, and get some advertisement clicks on the side; portrayed identities, on dating sites and elsewhere. Can a computer automatically detect deception in written accounts or in video recordings? In this talk, I will describe our work in building linguistic and multimodal algorithms for deception detection, targeting deceptive statements, trial videos, fake news, identity deceptions, and also going after deception in multiple cultures. I will also show how these algorithms can provide insights into what makes a good lie - and thus teach us how we can spot a liar. As it turns out, computers can be trained to identify lies in many different contexts, and they can do it much better than humans do!
ANS-Based Index Compression
Authors: Alistair Moffat, Matthias Petri
Venue: CIKM
Year: 2017

Techniques for effectively representing the postings lists associated with inverted indexes have been studied for many years. Here we combine the recently developed "asymmetric numeral systems" (ANS) approach to entropy coding and a range of previous index compression methods, including VByte, Simple, and Packed. The ANS mechanism allows each of them to provide markedly improved compression effectiveness, at the cost of slower decoding rates. Using the 426 GB GOV2 collection, we show that the combination of blocking and ANS-based entropy-coding against a set of 16 magnitude-based probability models yields compression effectiveness superior to most previous mechanisms, while still providing reasonable decoding speed.
Privacy Aware Temporal Profiling of Emails in Distributed Setup
Authors: Sutapa Mondal, Manish Shukla, Sachin Lodha
Venue: CIKM
Year: 2017

The enterprise email promises to be a rich source for knowledge discovery. This is made possible due to the direct nature of communication, support for diverse media types, active participation of entities and presence of chronological ordering of messages. Also, the enterprise emails are more trustworthy than external emails due to their formal nature. This data source has not been fully tapped. In fact, the existing work on profiling of emails focuses primarily on expertise identification and retrieval. Even in these studies, the researchers have made some restrictive assumptions. For instance, in many of the formulations, the underlying system assumes a centralized data repository, and the communication network is complete. They do not account for individual biases in an email while mining and aggregating results. Furthermore, email holds fair amount of personal and organizational sensitive information. None of the existing work on email profiling suggests anything on alleviating the individual and organizational privacy concerns.
Learning Entity Type Embeddings for Knowledge Graph Completion
Authors: Changsung Moon, Paul Jones, Nagiza F. Samatova
Venue: CIKM
Year: 2017

Missing data is a severe problem for algorithms that operate over knowledge graphs (KGs). Most previous research in KG completion has focused on the problem of inferring missing entities and missing relation types between entities. However, in addition to these, many KGs also suffer from missing entity types (i.e. the category labels for entities, such as /music/artist). Entity types are a critical enabler for many NLP tasks that use KGs as a reference source, and inferring missing entity types remains an important outstanding obstacle in the field of KG completion. Inspired by recent work to build a contextual KG embedding model, we propose a novel approach to address the entity type prediction problem. We compare the performance of our method with several state-of-the-art KG embedding methods, and show that our approach gives higher prediction accuracy compared to baseline algorithms on two real-world datasets. Our approach also produces consistently high accuracy when inferring entities and relation types, as well as the primary task of inferring entity types. This is in contrast to many of the baseline methods that specialize in one prediction task or another. We achieve this while preserving linear scalability with the number of entity types. Source code and datasets from this paper can be found at (https://github.ncsu.edu/cmoon2/kg).