OpenRaiser/Intern-Atlas huggingface dataset:
CrowdTiles: presenting crowd-based information for event-driven information needs
Authors: Stewart Whiting, Ke Zhou, Joemon M. Jose, Omar Alonso, Teerapong Leelanupab
Venue: CIKM
Year: 2012

Time plays a central role in many web search information needs relating to recent events. For recency queries where fresh information is most desirable, there is likely to be a great deal of highly-relevant information created very recently by crowds of people across the world, particularly on platforms such as Wikipedia and Twitter. With so many users, mainstream events are often very quickly reflected in these sources. The English Wikipedia encyclopedia consists of a vast collection of user-edited articles covering a range of topics. During events, users collaboratively create and edit existing articles in near real-time. Simultaneously, users on Twitter disseminate and discuss event details, with a small number of users becoming influential for the topic.
PhotoFall: discovering weblog stories through photographs
Authors: Christopher Wienberg, Andrew S. Gordon
Venue: CIKM
Year: 2012

An effective means of retrieving relevant photographs from the web is to search for terms that would likely appear in the surrounding text in multimedia documents. In this paper, we investigate the complementary search strategy, where relevant multimedia documents are retrieved using the photographs they contain. We concentrate our efforts on the retrieval of large numbers of personal stories posted to Internet weblogs that are relevant to a particular search topic. Photographs are often included in posts of this sort, typically taken by the author during the course of the narrated events of the story. We describe a new story search tool, PhotoFall, which allows users to quickly find stories related to their topic of interest by judging the relevance of the photographs extracted from top search results. We evaluate the accuracy of relevance judgments made using this interface, and discuss the implications of the results for improving topic-based searches of multimedia content.
Mixed-initiative conversational system using question-answer pairs mined from the web
Authors: Wilson Wong, Lawrence Cavedon, John Thangarajah, Lin Padgham
Venue: CIKM
Year: 2012

One of the biggest bottlenecks for conversational systems is large-scale provision of suitable content. Our approach readily provides this without the need for custom-crafting. In this demonstration, we present the use of question-answer (QA) pairs mined from online question-and-answer websites to construct system utterances for a conversational agent. Our system uses QA pairs to formulate utterances that drive a conversation in addition to the answering of user questions as has been done in previous work. We use a collection of strategies that specify how and when the different parts of our question-answer pairs can be used and augmented with a small number of generic hand-crafted text snippets to generate natural and coherent system utterances.
Serial position effects of clicking behavior on result pages returned by search engines
Authors: Mingda Wu, Shan Jiang, Yan Zhang
Venue: CIKM
Year: 2012

Under the joint influence of the presentation of search results and users' browsing and clicking habits, the click probability distribution does not merely obey a monotonic decreasing Zipf function. In this paper, we present evidence that the click behavior on the entries of search engines' result pages is influenced by Serial Position Effect, which is independent of how these entries are ordered, and introduce a new function to characterize the click probability distribution.
Efficient logging for enterprise workloads on column-oriented in-memory databases
Authors: Johannes Wust, Joos-Hendrik Boese, Frank Renkes, Sebastian Blessing, Jens Krüger, Hasso Plattner
Venue: CIKM
Year: 2012

The introduction of a 64 bit address space in commodity operating systems and the constant drop in hardware prices made large capacities of main memory in the order of terabytes technically feasible and economically viable. Especially column-oriented in-memory databases are a promising platform to improve data management for enterprise applications. As in-memory databases hold the primary persistence in volatile memory, some form of recovery mechanism is required to prevent potential data loss in case of failures. Two desirable characteristics of any recovery mechanism are (1) that it has a minimal impact on the running system, and (2) that the system recovers quickly and without any data loss after a failure. This paper introduces an efficient logging mechanism for dictionary-compressed column structures that addresses these two characteristics by (1) reducing the overall log size by writing dictionary-compressed values and (2) allowing for parallel writing and reading of log files. We demonstrate the efficiency of our logging approach by comparing the resulting log-file size with traditional logical logging on a workload produced by a productive enterprise system.
Probabilistic macro behavioral targeting
Authors: Yusheng Xie, Yu Cheng, Daniel Honbo, Kunpeng Zhang, Ankit Agrawal, Alok N. Choudhary, Yi Gao, Jiangtao Gou
Venue: CIKM
Year: 2012

We investigate a class of emerging online marketing challenges in social networks; and formally, we define macro behavioral targeting (MBT) to be the marketing efforts that appeal to a massive targeted population with non-personalized broadcasting. Upon the problem formulation, we describe a probabilistic graphical model for MBT. In our model, we derive the prior distributions from scratch because existing applications of graphical model / Bayesian network cannot fully capture the unique characteristics of MBT. In the derivation, we propose an approximation method to circumvent an intractable situation where order statistics need be calculated from exponentially increasing computations. In the experiments, we present case studies on real Facebook data.
A hybrid approach for efficient provenance storage
Authors: Yulai Xie, Dan Feng, Zhipeng Tan, Lei Chen, Kiran-Kumar Muniswamy-Reddy, Yan Li, Darrell D. E. Long
Venue: CIKM
Year: 2012

Efficient provenance storage is an essential step towards the adoption of provenance. In this paper, we analyze the provenance collected from multiple workloads with a view towards efficient storage. Based on our analysis, we characterize the properties of provenance with respect to long term storage. We then propose a hybrid scheme that takes advantage of the graph structure of provenance data and the inherent duplication in provenance data. Our evaluation indicates that our hybrid scheme, a combination of web graph compression (adapted for provenance) and dictionary encoding, provides the best tradeoff in terms of compression ratio, compression time and query performance when compared to other compression schemes.
Efficient buffer management for piecewise linear representation of multiple data streams
Authors: Qing Xie, Jia Zhu, Mohamed A. Sharaf, Xiaofang Zhou, Chaoyi Pang
Venue: CIKM
Year: 2012

Piecewise Linear Representation (PLR) has been a widely used method for approximating data streams in the form of compact line segments. The buffer-based approach to PLR enables a semi-global approximation which relies on the aggregated processing of batches of streamed data so that to adjust and improve the approximation results. However, one challenge towards applying the buffer-based approach is allocating the necessary memory resources for stream buffering. This challenge is further complicated in a multi-stream environment where multiple data streams are competing for the available memory resources, especially in resource-constrained systems such as sensors and mobile devices.
Do ads compete or collaborate?: designing click models with full relationship incorporated
Authors: Xin Xin, Irwin King, Ritesh Agrawal, Michael R. Lyu, Heyan Huang
Venue: CIKM
Year: 2012

Traditionally click models predict click-through rate (CTR) of an advertisement (ad) independent of other ads. Recent researches however indicate that the CTR of an ad is dependent on the quality of the ad itself but also of the neighboring ads. Using historical click-through data of a commercially available ad server, we identify two types (competing and collaborating) of influences among sponsored ads and further propose a novel click-model, Full Relation Model (FRM), which explicitly models dependencies between ads. On a test data, FRM shows significant improvement in CTR prediction as compared to earlier click models.
A picture paints a thousand words: a method of generating image-text timelines
Authors: Shize Xu, Liang Kong, Yan Zhang
Venue: CIKM
Year: 2012

Manual timelines have greatly helped us to keep pace with the big world. In this paper, we introduce a novel solution which generates image-text timelines for news events based on Evolutionary Image-Text Summarization, which is an important and challenging problem. We first extract image's semantic information under translation model, and then fuse the high quality images with text timeline under an image assignment algorithm which can optimize the global coordination of the final timeline. The experimental results show that news readers can receive more satisfaction from the image-text timelines we generate.
Feature selection for link prediction
Authors: Ye Xu, Daniel N. Rockmore
Venue: CIKM
Year: 2012

Networks that model relationships in the real world have attracted much attention in the past few years. Link prediction plays a central role in the network area. Supervised learning is an important class of algorithms used to address the link prediction problem. A big challenge in solving link prediction tasks is deciding how to choose relevant features. As an important machine learning technique to select relevant features, feature selection not only enhances classification accuracy, but also improves the efficiency of the training process. Thus, it is especially relevant for link prediction. However, to the best of our knowledge, feature selection under the link prediction scenario remains unstudied. In this paper, we propose FEature Selection for Link Prediction (FESLP), which contains a feature ranking algorithm and a feature weighting algorithm to address link prediction tasks. We measure the discriminative ability of each individual feature and select those features with greatest discriminative power. Simultaneously, we aim to minimize the correlations among features such that redundancy in the learned feature space is as small as possible. Thus, the feature space can accurately preserve the sketch of the original data. Feature weighting and feature ranking problems can be formalized as two quadratic optimization problems. The active set method is used to solve the feature weighting problem (via real number programming) while a greedy policy is applied to solve the feature ranking problem (via integer programming). In experiments, We evaluate FESLP on six large-scale email network datasets from a university. The results show the effectiveness of the FESLP.
Towards jointly extracting aspects and aspect-specific sentiment knowledge
Authors: Xueke Xu, Songbo Tan, Yue Liu, Xueqi Cheng, Zheng Lin
Venue: CIKM
Year: 2012

In this paper, we aim to jointly extract aspects and aspect-specific sentiment knowledge from online reviews, where the sentiment knowledge refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities. To this end, we propose a Joint Aspect/Sentiment model (JAS). JAS detects aspect-specific opinion words by integrating opinion word lexicon knowledge to explicitly separate opinion words from factual words. More importantly, JAS exploits sentiment prior and aspect-contextual sentence-level co-occurrences of opinion words in reviews to further identify aspect-aware sentiment polarities for the opinion words. We apply the learned aspect-specific sentiment knowledge to practical aspect-level sentiment analysis tasks. Experimental results show the effectiveness of JAS in learning aspect-specific sentiment knowledge and the practical value of this knowledge when applied to aspect-level sentiment classification.
Multiview hierarchical bayesian regression model andapplication to online advertising
Authors: Tianbing Xu, Ruofei Zhang, Zhen Guo
Venue: CIKM
Year: 2012

With the development of Web applications, large scale data are popular; and they are not only getting richer, but also ubiquitously interconnected with users and other objects in various ways, which brings about multi-view data with implicit structure. In this paper, we propose a novel hierarchical Bayesian mixture regression model, which discovers and then exploits the relationships among multiple views of the data to perform various machine learning tasks. A stochastic EM inference and learning algorithm is derived; and a parallel implementation in Hadoop MapReduce [9] paradigm is developed to scale up the learning. We apply the developed model and algorithm on click-through-rate (CTR) prediction and campaign targeting recommendation in online advertising to measure its effectiveness. The experiments on both synthetic data and large scale ads serving data from a real world online advertising exchange demonstrate the superior CTR prediction accuracy of our method compared to existing state-of-the-art methods. The results also show that our model can recommend high performance targeting features for online advertising campaigns.
Delineating social network data anonymization via random edge perturbation
Authors: Mingqiang Xue, Panagiotis Karras, Chedy Raïssi, Panos Kalnis, Hung Keng Pung
Venue: CIKM
Year: 2012

Social network data analysis raises concerns about the privacy of related entities or individuals. To address this issue, organizations can publish data after simply replacing the identities of individuals with pseudonyms, leaving the overall structure of the social network unchanged. However, it has been shown that attacks based on structural identification (e.g., a walk-based attack) enable an adversary to re-identify selected individuals in an anonymized network. In this paper we explore the capacity of techniques based on random edge perturbation to thwart such attacks. We theoretically establish that any kind of structural identification attack can effectively be prevented using random edge perturbation and show that, surprisingly, important properties of the whole network, as well as of subgraphs thereof, can be accurately calculated and hence data analysis tasks performed on the perturbed data, given that the legitimate data recipient knows the perturbation probability as well. Yet we also examine ways to enhance the walk-based attack, proposing a variant we call probabilistic attack. Nevertheless, we demonstrate that such probabilistic attacks can also be prevented under sufficient perturbation. Eventually, we conduct a thorough theoretical study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments with synthetic and real datasets confirm our expectations.
An architecture for personalized health information retrieval
Authors: Nikhil Yadav, Christian Poellabauer
Venue: CIKM
Year: 2012

With the rapid proliferation of the Internet, traditional Information Retrieval (IR) techniques need to address challenges that stem from information overload by filtering web documents and ranking them in an order that can be perceived to be more relevant and credible to the end-user. In the domain of health care, an increasing number of people turn to the Internet for their health and wellness concerns. The results returned by traditional search engines can therefore be overwhelming and, even worse, inaccurate. As a consequence there is a need to design more "intelligent" web services that pre-process and alter information on the user's behalf. Specifically, this paper describes the design of a personalized search engine that utilizes patient data (either stored in user-managed personal health records or in provider-managed electronic medical records) and couples this with a selective crawling of credible medical information to eliminate search results that appear irrelevant to the user (given the user's "health profile") and rank the remaining results in order of relevance based on the health conditions of users performing the searches. Toward this end, a new ranking algorithm that combines a user's search query and the user's health profile is introduced. Finally, comparisons of the search results for users with different health profiles and diverse queries are presented using this architecture.
Clustering short text using Ncut-weighted non-negative matrix factorization
Authors: Xiaohui Yan, Jiafeng Guo, Shenghua Liu, Xueqi Cheng, Yanfeng Wang
Venue: CIKM
Year: 2012

Non-negative matrix factorization (NMF) has been successfully applied in document clustering. However, experiments on short texts, such as microblogs, Q&A documents and news titles, suggest unsatisfactory performance of NMF. An major reason is that the traditional term weighting schemes, like binary weight and tfidf, cannot well capture the terms' discriminative power and importance in short texts, due to the sparsity of data. To tackle this problem, we proposed a novel term weighting scheme for NMF, derived from the Normalized Cut (Ncut) problem on the term affinity graph. Different from idf, which emphasizes discriminability on document level, the Ncut weighting measures terms' discriminability on term level. Experiments on two data sets show our weighting scheme significantly boosts NMF's performance on short text clustering.
Leveraging read rates of passive RFID tags for real-time indoor location tracking
Authors: Da Yan, Zhou Zhao, Wilfred Ng
Venue: CIKM
Year: 2012

RFID (radio frequency identification) technology has been widely used for object tracking in many real-life applications, such as inventory monitoring and product flow tracking. These applications usually rely on passive RFID technologies rather than active ones, since passive RFID tags are more attractive than active ones in many aspects, such as lower tag cost and simpler maintenance.
Monochromatic and bichromatic reverse nearest neighbor queries on land surfaces
Authors: Da Yan, Zhou Zhao, Wilfred Ng
Venue: CIKM
Year: 2012

Finding reverse nearest neighbors (RNNs) is an important operation in spatial databases. The problem of evaluating RNN queries has already received considerable attention due to its importance in many real-world applications, such as resource allocation and disaster response. While RNN query processing has been extensively studied in Euclidean space, no work ever studies this problem on land surfaces. However, practical applications of RNN queries involve terrain surfaces that constrain object movements, which rendering the existing algorithms inapplicable.
SHB 2012: international workshop on smart health and wellbeing
Authors: Christopher C. Yang, Hsinchun Chen, Howard D. Wactlar, Carlo Combi, Xuning Tang
Venue: CIKM
Year: 2012

The Smart Health and Wellbeing workshop is organized to develop a platform for authors to discuss fundamental principles, algorithms or applications of intelligent data acquisition, processing and analysis of healthcare data. We are particularly interested in information and knowledge management papers, in which the approaches are accompanied by an in-depth experimental evaluation with real world data. This paper provides an overview of the workshop and the accepted contributions.
On bundle configuration for viral marketing in social networks
Authors: De-Nian Yang, Wang-Chien Lee, Nai-Hui Chia, Mao Ye, Hui-Ju Hung
Venue: CIKM
Year: 2012

Prior research on viral marketing mostly focuses on promoting one single product item. In this work, we explore the idea of bundling multiple items for viral marketing and formulate a new research problem, called Bundle Configuration for SpreAd Maximization (BCSAM). Efficiently obtaining an optimal product bundle under the setting of BCSAM is very challenging. Aiming to strike a balance between the quality of solution and the computational overhead, we systematically explore various heuristics to develop a suite of algorithms, including κ-Bundle Configuration and Aggregated Bundle Configuration. Moreover, we integrate all the proposed ideas into one efficient algorithm, called Aggregated Bundle Configuration (ABC). Finally, we conduct an extensive performance evaluation on our proposals. Experimental results show that ABC significantly outperforms its counterpart and two baseline approaches in terms of both computational overhead and bundle quality.
Finding the optimal path over multi-cost graphs
Authors: Yajun Yang, Jeffrey Xu Yu, Hong Gao, Jianzhong Li
Venue: CIKM
Year: 2012

Shortest path query is an important problem in graphs and has been well-studied. However, most approaches for shortest path query are based on single-cost (weight) graphs. In this paper, we introduce the definition of multi-cost graph and study a novel query: the optimal path query over multi-cost graphs. We propose a best-first branch and bound search algorithm with two optimizing strategies. Furthermore, we propose a novel index named k-cluster index to make our method more space and time efficient for large graphs. We discuss how to construct and utilize k-cluster index. We confirm the effectiveness and efficiency of our algorithms using real-life datasets in experiments.
Social media mining for drug safety signal detection
Authors: Christopher C. Yang, Haodong Yang, Ling Jiang, Mi Zhang
Venue: CIKM
Year: 2012

Adverse Drug Reactions (ADRs) represent a serious problem all over the world. They may complicate a patient's medical conditions and increase the morbidity, even mortality. Drug safety currently depends heavily on post-marketing surveillance, because pre-marketing review process cannot identify all possible adverse drug reactions in that it is limited by scale and time span. However, current post-marketing surveillance is conducted through centralized volunteering reporting systems, and the reporting rate is low. Consequently, it is difficult to detect the adverse drug reactions signals in a timely manner. To solve this problem, many researchers have explored methods to detect ADRs in electronic health records. Nevertheless, we only have access to electronic health records form particular health units. Aggregating and integrating electronic health records from multiple sources is rather challenging. With the advance of Web 2.0 technologies and the popularity of social media, many health consumers are discussing and exchanging health-related information with their peers. Many of this online discussion involve adverse drug reactions. In this work, we propose to use association mining and Proportional Reporting Ratios to mine the associations between drugs and adverse reactions from the user contributed content in social media. We have conducted an experiment using ten drugs and five adverse drug reactions. The FDA alerts are used as the gold standard to test the performance of the proposed techniques. The result shows that the metrics leverage, lift, and PRR are all promising to detect the adverse drug reactions reported by FDA. However, PRR outperformed the other two metrics.