OpenRaiser/Intern-Atlas huggingface dataset:
Tell Me What You Want and I Will Tell Others Where You Have Been
Authors: Anthony Quattrone, Elham Naghizade, Lars Kulik, Egemen Tanin
Venue: CIKM
Year: 2014

Trajectory data does not only show the location of users over a period of time, but also reveals a high level of detail regarding their lifestyle, preferences and habits. Hence, it is highly susceptible to privacy concerns. Trajectory privacy has become a key research topic when sharing/exchanging trajectory datasets. Most existing studies focus on protecting trajectory data through obfuscating, anonymising or perturbing the data with the aim to maximize user privacy. Although such approaches appear plausible, our work suggests that precise trajectory information can be inferred even from other sources of data. We consider the case in which a location service provider only shares POI query results of users with third parties instead of exchanging users' raw trajectory data to preserve privacy. We develop an inference algorithm and show that it can effectively approximate original trajectories using solely the POI query results.
Axiomatic Analysis of Cross-Language Information Retrieval
Authors: Razieh Rahimi, Azadeh Shakery, Irwin King
Venue: CIKM
Year: 2014

A major challenge in Cross-Language Information Retrieval (CLIR) is the adoption of translation knowledge in retrieval models, as it affects the term weighting which is known to highly impact the retrieval performance. In this paper, we present an analytical study of using translation knowledge in CLIR. In particular, by adopting axiomatic analysis framework, we formulate the impacts of translation knowledge on document ranking as constraints that any cross-language retrieval model should satisfy. We then consider the state-of-the-art CLIR methods and check whether they satisfy these constraints. Finally, we show through empirical evaluation that violating one of the constraints harms the retrieval performance significantly which calls for further investigation.
Sampling Triples from Restricted Networks using MCMC Strategy
Authors: Mahmudur Rahman, Mohammad Al Hasan
Venue: CIKM
Year: 2014

In large networks, the connected triples are useful for solving various tasks including link prediction, community detection, and spam filtering. Existing works in this direction concern mostly with the exact or approximate counting of connected triples that are closed (aka, triangles). Evidently, the task of triple sampling has not been explored in depth, although sampling is a more fundamental task than counting, and the former is useful for solving various other tasks, including counting. In recent years, some works on triple sampling have been proposed that are based on direct sampling, solely for the purpose of triangle count approximation. They sample only from a uniform distribution, and are not effective for sampling triples from an arbitrary user-defined distribution. In this work we present two indirect triple sampling methods that are based on Markov Chain Monte Carlo (MCMC) sampling strategy. Both of the above methods are highly efficient compared to a direct sampling-based method, specifically for the task of sampling from a non-uniform probability distribution. Another significant advantage of the proposed methods is that they can sample triples from networks that have restricted access, on which a direct sampling based method is simply not applicable.
Forest-Based Dynamic Sorted Neighborhood Indexing for Real-Time Entity Resolution
Authors: Banda Ramadan, Peter Christen
Venue: CIKM
Year: 2014

Real-time entity resolution (ER) is the process of matching a query record in sub-second time with records in a database that represent the same real-world entity. To facilitate real-time matching on large databases, appropriate indexing approaches are required to reduce the search space. Most available indexing techniques are based on batch algorithms that work only with static databases and are not suitable for real-time ER. In this paper, we propose a forest-based sorted neighborhood index that uses multiple index trees with different sorting keys to facilitate real-time ER for read-most databases. Our technique aims to reduce the effect of errors and variations in attribute values on matching quality by building several distinct index trees. We conduct an experimental evaluation on two large real-world data sets, and multiple synthetic data sets with various data corruption rates. The results show that our approach is scalable to large databases and that using multiple trees gives a noticeable improvement on matching quality with only a small increase in query time. Our approach also achieves over one order of magnitude faster indexing and querying times, as well as higher matching accuracy, compared to another recently proposed real-time ER technique.
How People Use the Web in Large Indoor Spaces
Authors: Yongli Ren, Martin Tomko, Kevin Ong, Mark Sanderson
Venue: CIKM
Year: 2014

We report a preliminary study of mobile Web behaviour in a large indoor retail space. By analysing a Web log collected over a 1 year period at an inner city shopping mall in Sydney, Australia, we found that 1) around 60% of registered Wi-Fi users actively browse the Internet, and the rest 40% do not, with around 10% of these users using Web search engines. Around 70% of this Web activity in the investigated mall come from frequent visitors; 2) the content that indoor users search for is different from the content they consume while browsing; 3) the popularity of future indoor search queries can be predicted with a simple theoretical model based on past queries treated as a weighted directed graph. The work described in this paper underpins applications such as the prediction of users' information needs, retail recommendation systems, and improving the mobile Web search experience.
Competitive Game Designs for Improving the Cost Effectiveness of Crowdsourcing
Authors: Markus Rokicki, Sergiu Chelaru, Sergej Zerr, Stefan Siersdorfer
Venue: CIKM
Year: 2014

Crowd based online work is leveraged in a variety of applications such as semantic annotation of images, translation of texts in foreign languages, and labeling of training data for machine learning models. However, annotating large amounts of data through crowdsourcing can be slow and costly. In order to improve both cost and time efficiency of crowdsourcing we examine alternative reward mechanisms compared to the "Pay-per-HIT" scheme commonly used in platforms such as Amazon Mechanical Turk. To this end, we explore a wide range of monetary reward schemes that are inspired by the success of competitions, lotteries, and games of luck. Our large-scale experimental evaluation with an overall budget of more than 1,000 USD and with 2,700 hours of work spent by crowd workers demonstrates that our alternative reward mechanisms are well accepted by online workers and lead to substantial performance boosts.
Enterprise Discussion Analysis
Authors: Sara Rosenthal, Ashish Jagmohan
Venue: CIKM
Year: 2014

Recent business studies have shown that social technologies can significantly improve productivity within enterprises by improving access to information, ideas, and collaborators. A manifestation of the growing adoption of enterprise social technologies is the increasing use of enterprise virtual discussions to engage customers and employees. In this paper we present an enterprise discussion analysis system which seeks to enable rapid interactive inference of insights from virtual online enterprise discussions. Rapid understanding is facilitated by extracting a hierarchy of key concepts, which represent a multi-faceted thematic categorization of discussion content, and by identifying high-quality thematic exemplar comments. The concept hierarchy and exemplar comments are presented through an intuitive web user-interface which allows an analyst to quickly navigate through the main concepts and the most relevant comments extracted from the discussion. We present a preliminary validation of system efficacy through user surveys provided to test users.
Designing Test Collections for Comparing Many Systems
Authors: Tetsuya Sakai
Venue: CIKM
Year: 2014

A researcher decides to build a test collection for comparing her new information retrieval (IR) systems with several state-of-the-art baselines. She wants to know the number of topics (n) she needs to create in advance, so that she can start looking for (say) a query log large enough for sampling n good topics, and estimating the relevance assessment cost. We provide practical solutions to researchers like her using power analysis and sample size design techniques, and demonstrate its usefulness for several IR tasks and evaluation measures. We consider not only the paired t-test but also one-way analysis of variance (ANOVA) for significance testing to accommodate comparison of m(≥ 2) systems under a given set of statistical requirements (α: the Type I error rate, ß: the Type II error rate, and minD: the minimum detectable difference between the best and the worst systems). Using our simple Excel tools and some pooled variance estimates from past data, researchers can design statistically well-designed test collections. We demonstrate that, as different evaluation measures have different variances across topics, they inevitably require different topic set sizes. This suggests that the evaluation measures should be chosen at the test collection design phase. Moreover, through a pool depth reduction experiment with past data, we show how the relevance assessment cost can be reduced dramatically while freezing the set of statistical requirements. Based on the cost analysis and the available budget, researchers can determine the right balance between n and the pool depth pd. Our techniques and tools are applicable to test collections for non-IR tasks as well.
Solving Linear SVMs with Multiple 1D Projections
Authors: Johannes Schneider, Jasmina Bogojeska, Michail Vlachos
Venue: CIKM
Year: 2014

We present a new methodology for solving linear Support Vector Machines (SVMs) that capitalizes on multiple 1D projections. We show that the approach approximates the optimal solution with high accuracy and comes with analytical guarantees. Our solution adapts on methodologies from random projections, exponential search, and coordinate descent. In our experimental evaluation, we compare our approach with the popular liblinear SVM library. We demonstrate a significant speedup on various benchmarks. At the same time, the new methodology provides a comparable or better approximation factor of the optimal solution and exhibits smooth convergence properties. Our results are accompanied by bounds on the time complexity and accuracy.
CONDOR: A System for CONstraint DiscOvery and Repair
Authors: Joshua Segeren, Dhruv Gairola, Fei Chiang
Venue: CIKM
Year: 2014

We present CONDOR, a tool for managing constraints towards improved data quality. As increasing amounts of heterogeneous data are being generated, integrity constraints are the primary tool for enforcing data integrity. It is essential that an accurate and up-to-date set of constraints exist to validate that the correct application semantics are being enforced. We consider the widely used constraint, functional dependencies (FDs). CONDOR is an integrated system that identifies inconsistent data values (along with suggestions for clean values), and generates repairs to both the data and/or FDs to resolve inconsistencies. We extend the set of FD repair operations proposed in past work, by (1) adding a set of attributes to an FD; (2) transforming an FD to a conditional functional dependency (CFD); and (3) identifying redundant attributes in an FD. Our demonstration will showcase the visualization and interactive features of CONDOR to help users determine the best repairs that resolve the underlying inconsistencies to improve data quality.
A Problem-Action Relation Extraction Based on Causality Patterns of Clinical Events in Discharge Summaries
Authors: Jae-Wook Seol, Seung-Hyeon Jo, Wangjin Yi, Jinwook Choi, Kyung-Soon Lee
Venue: CIKM
Year: 2014

Medical knowledge extraction has great potential to improve the treatment quality of hospitals. In this paper, we propose a clinical problem-action relation extraction method. It is based on clinical semantic units and event causality patterns in order to present a chronological view of a patient's problem and a physician's action. Based on our observation, a clinical semantic unit is defined as a conceptual medical knowledge for a problem and/or action. Since a clinical event is a basic concept of the problem-action relation, events are detected from clinical texts based on conditional random fields. A clinical semantic unit is segmented from a sentence based on time expressions and inherent structure of events. Then, a clinical semantic unit is classified into a problem and/or action relation based on event causality features in support vector machines. The experimental result on Korean medical collection shows 78.8% in F-measure when given the answer of clinical events. This result shows that the proposed method is effective for extracting clinical problem-action relations.
Travel distance versus navigation complexity: a study on different spatial queries on road networks
Authors: Jie Shao, Lars Kulik, Egemen Tanin, Long Guo
Venue: CIKM
Year: 2014

Research on cognitive science indicates that humans often use different criteria for route selection. An alternative type of spatial proximity search on road networks recently has been proposed to find the easiest-to-reach neighboring object with the smallest navigation complexity. This paper presents an evaluation to compare the effectiveness of easiest-to-reach neighbor query against a classic nearest neighbor query in a real-world setting. Our user study demonstrates usability of the new spatial query type and suggests people may not always care about travel distance most. To provide flexibility to accommodate different requirements, we also show how to achieve tradeoff between navigation complexity and travel distance for advanced navigational assistance.
Controllable Information Sharing for User Accounts Linkage across Multiple Online Social Networks
Authors: Yilin Shen, Hongxia Jin
Venue: CIKM
Year: 2014

People have multiple accounts on Online Social Networks (OSNs) for various purposes. It is of great interest for third parties to collect more users' information by linking their accounts on different OSNs. Unfortunately, most users have not been aware of potential risks of such accounts linkage. Therefore, the design of a control methodology that allows users to share their information without the risk of being linked becomes an urgent need, yet still remains open.
CARS2: Learning Context-aware Representations for Context-aware Recommendations
Authors: Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha A. Larson, Alan Hanjalic
Venue: CIKM
Year: 2014

Rich contextual information is typically available in many recommendation domains allowing recommender systems to model the subtle effects of context on preferences. Most contextual models assume that the context shares the same latent space with the users and items. In this work we propose CARS2, a novel approach for learning context-aware representations for context-aware recommendations. We show that the context-aware representations can be learned using an appropriate model that aims to represent the type of interactions between context variables, users and items. We adapt the CARS2 algorithms to explicit feedback data by using a quadratic loss function for rating prediction, and to implicit feedback data by using a pairwise and a listwise ranking loss functions for top-N recommendations. By using stochastic gradient descent for parameter estimation we ensure scalability. Experimental evaluation shows that our CARS2 models achieve competitive recommendation performance, compared to several state-of-the-art approaches.
Ranking-based Clustering on General Heterogeneous Information Networks by Network Projection
Authors: Chuan Shi, Ran Wang, Yitong Li, Philip S. Yu, Bin Wu
Venue: CIKM
Year: 2014

Recently there is an increasing attention in heterogeneous information network analysis, which models networked data as networks including different types of objects and relations. Many data mining tasks have been exploited in heterogeneous networks, among which clustering and ranking are two basic tasks. These two tasks are usually done separately, whereas recent researches show that they can mutually enhance each other. Unfortunately, these works are limited to heterogeneous networks with special structures (e.g. bipartite or star-schema network). However, real data are more complex and irregular, so it is desirable to design a general method to manage objects and relations in heterogeneous networks with arbitrary schema. In this paper, we study the ranking-based clustering problem in a general heterogeneous information network and propose a novel solution HeProjI. HeProjI projects a general heterogeneous network into a sequence of sub-networks and an information transfer mechanism is designed to keep the consistency among sub-networks. For each sub-network, a path-based random walk model is built to estimate the reachable probability of objects which can be used for clustering and ranking analysis. Iteratively analyzing each sub-network leads to effective ranking-based clustering. Extensive experiments on three real datasets illustrate that HeProjI can achieve better clustering and ranking performances compared to other well-established algorithms.
Deal or deceit: detecting cheating in distribution channels
Authors: Kai Shu, Ping Luo, Wan Li, Peifeng Yin, Linpeng Tang
Venue: CIKM
Year: 2014

Distribution channel is a system that partners move products from manufacturer to end users. To increase sales, it is quite common for manufacturers to adjust the product prices to partners according to the product volume per deal. However, the price adjustment is like a double-edged sword. It also spurs some partners to form a cheating alliance, where a cheating seller applies for a falsified big deal with a low price and then re-sells the products to the cheating buyers. Since these cheating behaviors are harmful to a healthy ecosystem of distribution channel, we need the automatic method to guide the tedious audit process.
Mining Semi-Structured Online Knowledge Bases to Answer Natural Language Questions on Community QA Websites
Authors: Parikshit Sondhi, ChengXiang Zhai
Venue: CIKM
Year: 2014

Over the past few years, community QA websites (e.g. Yahoo! Answers) have become a useful platform for users to post questions and obtain answers. However, not all questions posted there receive informative answers or are answered in a timely manner. In this paper, we show that the answers to some of these questions are available in online domain-specific knowledge bases and propose an approach to automatically discover those answers. In the proposed approach, we would first mine appropriate SQL query patterns by leveraging an existing collection of QA pairs, and then use the learned query patterns to answer previously unseen questions by returning relevant entities from the knowledge base. Evaluation on a collection of health domain questions from Yahoo! Answers shows that the proposed method is effective in discovering potential answers to user questions from an online medical knowledge base.
Aroma: A New Data Protection Method with Differential Privacy and Accurate Query Answering
Authors: Chunyao Song, Tingjian Ge
Venue: CIKM
Year: 2014

We propose a new local data perturbation method called Aroma. We first show that Aroma is sound in its privacy protection. For that, we devise a realistic privacy game, called the exposure test. We prove that the αβ algorithm, a previously proposed method that is most closely related to Aroma, performs poorly under the exposure test and fails to provide sufficient privacy in practice. Moreover, any data protection method that satisfies ε-differential privacy will succeed in the test. By proving that Aroma satisfies ε-differential privacy, we show that Aroma offers strong privacy protection. We then demonstrate the utility of Aroma by proving that its estimator has significantly smaller errors than the previous state-of-the-art algorithms such as αβ, AM, and FRAPP. We carry out a systematic empirical study using real-world data to evaluate Aroma, which shows its clear advantages over previous methods.
People Search within an Online Social Network: Large Scale Analysis of Facebook Graph Search Query Logs
Authors: Nikita V. Spirin, Junfeng He, Mike Develin, Karrie G. Karahalios, Maxime Boucher
Venue: CIKM
Year: 2014

Popular online social networks (OSN) generate hundreds of terabytes of new data per day and connect millions of users. To help users cope with the immense scale and influx of new information, OSNs provide a search functionality. However, most of the search engines in OSNs today only support keyword queries and provide basic faceted search capabilities overlooking serendipitous network exploration and search for relationships between OSN entities. This results in siloed information and a limited search space. In 2013 Facebook introduced its innovative Graph Search product with the goal to take the OSN search experience to the next level and facilitate exploration of the Facebook Graph beyond the first degree. In this paper we explore people search on Facebook by analyzing an anonymized social graph, anonymized user profiles, and large scale anonymized query logs generated by users of Facebook Graph Search. We uncover numerous insights about people search across several demographics. We find that named entity and structured queries complement each other across one's duration on Facebook, that females search for people proportionately more than males, and that users submit more queries as they gain more friends. We introduce the concept of a lift predicate and highlight how a graph distance varies with the search goal. Based on these insights, we present a set of design implications to guide the research and development of the OSN search in the future.
Accelerometer-based Activity Recognition on Smartphone
Authors: Xing Su, Hanghang Tong, Ping Ji
Venue: CIKM
Year: 2014

Smartphones are ubiquitous and becoming more and more sophisticated, with ever-growing computing, networking and sensing powers. How can we help the users form a healthy habit by sending a reminder if s/he is sitting too long? How can we localize where we are inside a building and/or find the reception desk? Recognizing the physical activities (e.g., sitting, walking, jogging, etc) is a core building block to answer these questions and many more.
A Word-Scale Probabilistic Latent Variable Model for Detecting Human Values
Authors: Yasuhiro Takayama, Yoichi Tomiura, Emi Ishita, Douglas W. Oard, Kenneth R. Fleischmann, An-Shou Cheng
Venue: CIKM
Year: 2014

This paper describes a probabilistic latent variable model that is designed to detect human values such as justice or freedom that a writer has sought to reflect or appeal to when participating in a public debate. The proposed model treats the words in a sentence as having been chosen based on specific values; values reflected by each sentence are then estimated by aggregating values associated with each word. The model can determine the human values for the word in light of the influence of the previous word. This design choice was motivated by syntactic structures such as noun+noun, adjective+noun, and verb+adjective. The classifier based on the model was evaluated on a test collection containing 102 manually annotated documents focusing on one contentious political issue - Net neutrality, achieving the highest reported classification effectiveness for this task. We also compared our proposed classifier with human second annotator. As a result, the proposed classifier effectiveness is statistically comparable with human annotators.