OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic effects of ad impressions on commercial actions in display advertising
Authors: Joel Barajas, Ram Akella, Marius Holtan, Jaimie Kwon, Aaron Flores, Victor Andrei
Venue: CIKM
Year: 2012

In this paper, we develop a time series approach, based on Dynamic Linear Models (DLM), to estimate the impact of ad impressions on the daily number of commercial actions when no user tracking is possible. The proposed method uses aggregate data, and hence it is simple to implement without expensive infrastructure. Specifically, we model the impact of daily number of ad impressions in daily number of commercial actions. We incorporate persistence of campaign effects on actions assuming a decay factor. We relax the assumption of a linear impact of ads on actions using the log-transformation. We also account for outliers with long-tailed distributions fitted and estimated automatically without a pre-defined threshold. This is applied to observational data post-campaign and does not require an experimental set-up. We apply the method to data from one commercial ad network on 2,885 campaigns for 1,251 products during six months, to calibrate and perform model selection. We set up a randomized experiment for two campaigns where user tracking is feasible. We find that the output of the proposed method is consistent with the results of A/B testing with similar confidence intervals.
Generically extending anonymization algorithms to deal with successive queries
Authors: Manuel Barbosa, Alexandre Pinto, Bruno Gomes
Venue: CIKM
Year: 2012

This paper addresses the scenario of multi-release anonymization of datasets. We consider dynamic datasets where data can be inserted and deleted, and view this scenario as a case where each release is a small subset of the dataset corresponding, for example, to the results of a query. Compared to multiple releases of the full database, this has the obvious advantage of faster anonymization. We present an algorithm for post-processing anonymized queries that prevents anonymity attacks using multiple released queries. This algorithm can be used with several distinct protection principles and anonymization algorithms, which makes it generic and flexible. We give an experimental evaluation of the algorithm and compare it to $m$-invariance both in terms of efficiency and data quality. To this end, we propose two data quality metrics based on Shannon's entropy, and show that they can be seen as a refinement of existing metrics.
Discovering conditional inclusion dependencies
Authors: Jana Bauckmann, Ziawasch Abedjan, Ulf Leser, Heiko Müller, Felix Naumann
Venue: CIKM
Year: 2012

Data dependencies are used to improve the quality of a database schema, to optimize queries, and to ensure consistency in a database. Conditional dependencies have been introduced to analyze and improve data quality. A conditional dependency is a dependency with a limited scope defined by conditions over one or more attributes. Only the matching part of the instance must adhere to the dependency. In this paper we focus on conditional inclusion dependencies (CINDs).We generalize the definition of CINDs, distinguishing covering and completeness conditions. We present a new use case for such CINDs showing their value for solving complex data quality tasks. Further, we propose efficient algorithms that identify covering and completeness conditions conforming to given quality thresholds. Our algorithms choose not only the condition values but also the condition attributes automatically. Finally, we show that our approach efficiently provides meaningful and helpful results for our use case.
Probabilistic ranking in fuzzy object databases
Authors: Thomas Bernecker, Tobias Emrich, Hans-Peter Kriegel, Matthias Renz, Andreas Züfle
Venue: CIKM
Year: 2012

Ranking queries have been investigated extensively in the past due to their broad range of applications. In this paper, we study this problem in the context of fuzzy objects that have indeterministic boundaries. Fuzzy objects play an important role in many areas, such as biomedical image databases and GIS. To the best of our knowledge, we present the first efficient approach for similarity ranking in fuzzy object databases. The main challenge of ranking fuzzy objects is that these objects consist of multiple instances, each associated with a probability. We propose a framework to transform fuzzy objects into probabilistic objects which can then be ranked using existing algorithms for probabilistic objects.
A scalable approach for performing proximal search for verbose patent search queries
Authors: Sumit Bhatia, Bin He, Qi He, W. Scott Spangler
Venue: CIKM
Year: 2012

Even though queries received by traditional information retrieval systems are quite short, there are many application scenarios where long natural language queries are more effective. Further, incorporating term position information can help improve results of long queries. However, the techniques for incorporating term position information have been developed for terse queries and hence, can not be directly applied to long queries. Though there exist some methods for performing proximal search for long queries, they are not scalable due to long query response times. We describe an intuitive and simple, yet effective technique that implicitly incorporates term position information for long queries in a scalable manner. Our proposed approach achieves more than 700% faster query response times while maintaining the quality of retrieved results when compared with a state-of-the-art method for performing proximal search for very long queries.
Interactive pattern mining on hidden data: a sampling-based solution
Authors: Mansurul Bhuiyan, Snehasis Mukhopadhyay, Mohammad Al Hasan
Venue: CIKM
Year: 2012

Mining frequent patterns from a hidden dataset is an important task with 43 various real-life applications. In this research, we propose a solution to this problem that is based on Markov Chain Monte Carlo (MCMC) sampling of frequent patterns. Instead of returning all the frequent patterns, the proposed paradigm returns a small set of randomly selected patterns so that the clandestinity of the dataset can be maintained. Our solution also allows interactive sampling, so that the sampled patterns can fulfill the user's requirement effectively. We show experimental results from several real life datasets to validate the capability and usefulness of our solution; in particular, we show examples that by using our proposed solution, an eCommerce marketplace can allow pattern mining on user session data without disclosing the data to the public; such a mining paradigm helps the sellers of the marketplace, which eventually boost the marketplace's own revenue.
CDMW 2012 - city data management workshop: workshop summary
Authors: Veli Bicer, Thanh Tran, Fatma Ozcan, Opher Etzion
Venue: CIKM
Year: 2012

Cities today have become highly dense, dynamic living areas for the majority of planet's population and also focal points of innovation, commerce, and growth in a highly modernized world. Due to its intensifying importance, cities need to transform into sustainable, smarter and credible places to enable a tenantable and comfortable life for their citizens. City data, which is the source of our digitized knowledge about the cities, is a highly important element to achive this goal as it is the main input to build complex city ecosystems and to solve particular problems that are encountered in the cities today. In this respect, this workshop will provide a major forum to identify the challenges and opportunities in terms of better managing city data and to reveal its discriminating importance in various applications in a city ecosystem. As city data becomes more widespread and prevailing, it poses novel research problems which importantly are open to the investigation of a broad community of researchers in various fields.
You should read this! let me explain you why: explaining news recommendations to users
Authors: Roi Blanco, Diego Ceccarelli, Claudio Lucchese, Raffaele Perego, Fabrizio Silvestri
Venue: CIKM
Year: 2012

Recommender systems have become ubiquitous in content-based web applications, from news to shopping sites. Nonetheless, an aspect that has been largely overlooked so far in the recommender system literature is that of automatically building explanations for a particular recommendation. This paper focuses on the news domain, and proposes to enhance effectiveness of news recommender systems by adding, to each recommendation, an explanatory statement to help the user to better understand if, and why, the item can be her interest. We consider the news recommender system as a black-box, and generate different types of explanations employing pieces of information associated with the news. In particular, we engineer text-based, entity-based, and usage-based explanations, and make use of a Markov Logic Networks to rank the explanations on the basis of their effectiveness. The assessment of the model is conducted via a user study on a dataset of news read consecutively by actual users. Experiments show that news recommender systems can greatly benefit from our explanation module as it allows users to discriminate between interesting and not interesting news in the majority of the cases.
Tracing clusters in evolving graphs with node attributes
Authors: Brigitte Boden, Stephan Günnemann, Thomas Seidl
Venue: CIKM
Year: 2012

Data sources representing social networks with additional attribute information about the nodes are widely available in today's applications. Recently, combined clustering methods were introduced that consider graph information and attribute information simultaneously to detect meaningful clusters in such networks. In many cases, such attributed graphs also evolve over time. Therefore, there is a need for clustering methods that are able to trace clusters over different time steps and analyze their evolution over time. In this paper, we extend our combined clustering method DB-CSC to the analysis of evolving combined clusters.
Latent topics in graph-structured data
Authors: Christoph Böhm, Gjergji Kasneci, Felix Naumann
Venue: CIKM
Year: 2012

Large amounts of graph-structured data are emerging from various avenues, ranging from natural and life sciences to social and semantic web communities. We address the problem of discovering subgraphs of entities that reflect latent topics in graph-structured data. These topics are structured meta-information providing further insights into the data. The presented approach effectively detects such topics by exploiting only the structure of the underlying graph, thus avoiding the dependency on textual labels, which are a scarce asset in prevalent graph datasets. The viability of our approach is demonstrated in experiments on real-world datasets.
LINDA: distributed web-of-data-scale entity matching
Authors: Christoph Böhm, Gerard de Melo, Felix Naumann, Gerhard Weikum
Venue: CIKM
Year: 2012

Linked Data has emerged as a powerful way of interconnecting structured data on the Web. However, the cross-linkage between Linked Data sources is not as extensive as one would hope for. In this paper, we formalize the task of automatically creating "sameAs" links across data sources in a globally consistent manner. Our algorithm, presented in a multi-core as well as a distributed version, achieves this link generation by accounting for joint evidence of a match. Experiments confirm that our system scales beyond 100 million entities and delivers highly accurate results despite the vast heterogeneity and daunting scale.
Using social data for resume job matching
Authors: Jacob Bollinger, David Hardtke, Ben Martin
Venue: CIKM
Year: 2012

Bright has built an automated system for ranking job candidates against job descriptions. The candidate's resume and social media profiles are interwoven to build an augmented user profile. Similarly, the job description is augmented by external databases and user-generated content to build an enhanced job profile. These augmented user and job profiles are then analyzed in order to develop numerical overlap features each with strong discriminating power, and in sum with maximal coverage. The resulting feature scores are then combined into a single Bright Score using a custom algorithm, where the feature weights are derived from a nation-wide and controlled study in which we collected a large sample of human judgments on real resume-job pairings. We demonstrate that the addition of social media profile data and external data improves the classification accuracy dramatically in terms of identifying the most qualified candidates.
Frequent grams based embedding for privacy preserving record linkage
Authors: Luca Bonomi, Li Xiong, Rui Chen, Benjamin C. M. Fung
Venue: CIKM
Year: 2012

In this paper, we study the problem of privacy preserving record linkage which aims to perform record linkage without revealing anything about the non-linked records. We propose a new secure embedding strategy based on frequent variable length grams which allows record linkage on the embedded space. The frequent grams used for constructing the embedding base are mined from the original database under the framework of differential privacy. Compared with the state-of-the-art secure matching schema [15], our approach provides formal, provable privacy guarantees and achieves better scalability while providing comparable utility.
Extracting interesting association rules from toolbar data
Authors: Ilaria Bordino, Debora Donato, Barbara Poblete
Venue: CIKM
Year: 2012

Toolbar navigation logs provide rich data for enhancing information discovery on the Web. The value of this data resides in its scope, which goes beyond that of traditional query-mining data sources, such as search-engine logs. In this paper we present a methodology for extracting relevant association rules for queries, based on historic user navigational data. In addition, we propose a graph-based approach for extracting related queries and URLs for a given query.
An overview of electronic health information management systems quality assessment
Authors: Matt-Mouley Bouamrane, Frances S. Mair, Cui Tao
Venue: CIKM
Year: 2012

The efficient management and usage of information within integrated care delivery systems will have substantial impacts on patients' care outcomes. Electronic health information management systems need to guarantee the integrity of clinical data capture and the quality of information processing, in order to deliver actionable knowledge to health professionals at the point of care. Generic tools and evaluation frameworks are needed to assess the quality of eHealth information systems for a wide range of stakeholders: end-users, including health professionals and patients, healthcare organisations and policymakers.
The generalized dirichlet distribution in enhanced topic detection
Authors: Karla L. Caballero Espinosa, Joel Barajas, Ram Akella
Venue: CIKM
Year: 2012

We present a new, robust and computationally efficient Hierarchical Bayesian model for effective topic correlation modeling. We model the prior distribution of topics by a Generalized Dirichlet distribution (GD) rather than a Dirichlet distribution as in Latent Dirichlet Allocation (LDA). We define this model as GD-LDA. This framework captures correlations between topics, as in the Correlated Topic Model (CTM) and Pachinko Allocation Model (PAM), and is faster to infer than CTM and PAM. GD-LDA is effective to avoid over-fitting as the number of topics is increased. As a tree model, it accommodates the most important set of topics in the upper part of the tree based on their probability mass. Thus, GD-LDA provides the ability to choose significant topics effectively. To discover topic relationships, we perform hyper-parameter estimation based on Monte Carlo EM Estimation. We provide results using Empirical Likelihood(EL) in 4 public datasets from TREC and NIPS. Then, we present the performance of GD-LDA in ad hoc information retrieval (IR) based on MAP, [email protected], and Discounted Gain. We discuss an empirical comparison of the fitting time. We demonstrate significant improvement over CTM, LDA, and PAM for EL estimation. For all the IR measures, GD-LDA shows higher performance than LDA, the dominant topic model in IR. All these improvements with a small increase in fitting time than LDA, as opposed to CTM and PAM.
Variance maximization via noise injection for active sampling in learning to rank
Authors: Wenbin Cai, Ya Zhang
Venue: CIKM
Year: 2012

Active learning for ranking, which is to selectively label the most informative examples, has been widely studied in recent years. In this paper, we propose a general active learning for ranking strategy called Variance Maximization (VM). The algorithm relies on noise injection to perturb the original unlabeled examples and generate the rank distribution of each example. Using a DCG-like gain function to measure each ranked list sampled from the rank distribution, Variance Maximization selects the unlabeled example with the largest variance in the gain. The VM strategy is applied at both the query level and the document level, and a two-stage active learning algorithm is further derived. Experimental results on both the LETOR 4.0 dataset and a real-world Web search ranking dataset have demonstrated the effectiveness of the proposed active learning approach.
Time feature selection for identifying active household members
Authors: Pedro G. Campos, Alejandro Bellogín, Fernando Díez, Iván Cantador
Venue: CIKM
Year: 2012

Popular online rental services such as Netflix and MoviePilot often manage household accounts. A household account is usually shared by various users who live in the same house, but in general does not provide a mechanism by which current active users are identified, and thus leads to considerable difficulties for making effective personalized recommendations. The identification of the active household members, defined as the discrimination of the users from a given household who are interacting with a system (e.g. an on-demand video service), is thus an interesting challenge for the recommender systems research community. In this paper, we formulate the above task as a classification problem, and address it by means of global and local feature selection methods and classifiers that only exploit time features from past item consumption records. The results obtained from a series of experiments on a real dataset show that some of the proposed methods are able to select relevant time features, which allow simple classifiers to accurately identify active members of household accounts.
GTE: a distributional second-order co-occurrence approach to improve the identification of top relevant dates in web snippets
Authors: Ricardo Campos, Gaël Dias, Alípio Jorge, Celia Nunes
Venue: CIKM
Year: 2012

In this paper, we present an approach to identify top relevant dates in Web snippets with respect to a given user implicit temporal query. Our approach is two-fold. First, we propose a generic temporal similarity measure called GTE, which evaluates the temporal similarity between a query and a date. Second, we propose a classification model to accurately relate relevant dates to their corresponding query terms and withdraw irrelevant ones. We suggest two different solutions: a threshold-based classification strategy and a supervised classifier based on a combination of multiple similarity measures. We evaluate both strategies over a set of real-world text queries and compare the performance of our Web snippet approach with a query log based approach over the same set of queries. Experiments show that determining the most relevant dates of any given implicit temporal query can be improved with GTE combined with the second order similarity measure InfoSimba, the Dice coefficient and t
STFMap: query- and feature-driven visualization of large time series data sets
Authors: K. Selçuk Candan, Rosaria Rossini, Maria Luisa Sapino, Xiaolan Wang
Venue: CIKM
Year: 2012

Since many applications rely on time-based data, visualizing temporal data and helping experts explore large time series data sets are critical in many application domains. In this interactive system preview, we argue that time series often carry structural features that can, if efficiently identified and effectively visualized, help reduce visual overload and help the user quickly focus on the relevant portions of the data sets. Relying on this observation, we introduce a novel STFMap system, which includes four innovative query- and feature-driven time series data set visualization techniques: (a) segment-maps, (b) warp-maps, (c) stretch-maps, and (d) feature-maps. These rely on the salient temporal features of the time series and their alignments with respect to the given user query to help users explore the data set in a query-driven fashion.
Graph-based workflow recommendation: on improving business process modeling
Authors: Bin Cao, Jianwei Yin, ShuiGuang Deng, Dongjing Wang, Zhaohui Wu
Venue: CIKM
Year: 2012

How to improve the modeling efficiency and accuracy has become a burning problem. The popularization of recommendation technique in E-Commerce provide us new trajectories that can be used for addressing the problem. In this paper, we propose a graph-based workflow recommendation for improving business process modeling. The start point is so-called "workflow repository" including a set of already developed process models. Graph mining method is used to extract the process patterns from the repository. Based on graph edit distance (GED) [2], we calculate the distance between patterns and the partial business process, viewed as reference model, which is under modeling and select the candidate nodes with smaller distances for recommendation. The performance study show its feasibility for practical uses.
Incorporating variability in user behavior into systems based evaluation
Authors: Ben Carterette, Evangelos Kanoulas, Emine Yilmaz
Venue: CIKM
Year: 2012

Click logs present a wealth of evidence about how users interact with a search system. This evidence has been used for many things: learning rankings, personalizing, evaluating effectiveness, and more. But it is almost always distilled into point estimates of feature or parameter values, ignoring what may be the most salient feature of users---their variability. No two users interact with a system in exactly the same way, and even a single user may interact with results for the same query differently depending on information need, mood, time of day, and a host of other factors. We present a Bayesian approach to using logs to compute posterior distributions for probabilistic models of user interactions. Since they are distributions rather than point estimates, they naturally capture variability in the population. We show how to cluster posterior distributions to discover patterns of user interactions in logs, and discuss how to use the clusters to evaluate search engines according to a user model. Because the approach is Bayesian, our methods can be applied to very large logs (such as those possessed by Web search engines) as well as very small (such as those found in almost any other setting).
Automatically embedding newsworthy links to articles
Authors: Hakan Ceylan, Ioannis Arapakis, Pinar Donmez, Mounia Lalmas
Venue: CIKM
Year: 2012

It is of great interest to news providers such as Yahoo! News to attain higher visitor rates by promoting greater engagement with their content. One aspect of engagement deals with keeping users on the site longer by allowing them to navigate through content with enhanced, click-through experiences. News portals have invested in ways to provide embedded links within news stories. So far these links have been manually curated by professional editors, and due to the manual effort involved, the use of such links has been limited. In this paper we propose an automated approach to detecting and linking newsworthy events to associated articles. Our analysis, conducted on Amazon's Mechanical Turk, reveals that our system's performance is comparable to that of professional editors, and that users find the automatically generated highlights interesting and the associated articles worthy of reading.