OpenRaiser/Intern-Atlas huggingface dataset:
Where shall we go today?: planning touristic tours with tripbuilder
Authors: Igo Ramalho Brilhante, José Antônio Fernandes de Macêdo, Franco Maria Nardini, Raffaele Perego, Chiara Renso
Venue: CIKM
Year: 2013

In this paper we propose TripBuilder, a new framework for personalized touristic tour planning. We mine from Flickr the information about the actual itineraries followed by a multitude of different tourists, and we match these itineraries on the touristic Point of Interests available from Wikipedia. The task of planning personalized touristic tours is then modeled as an instance of the Generalized Maximum Coverage problem. Wisdom-of-the-crowds information allows us to derive touristic plans that maximize a measure of interest for the tourist given her preferences and visiting time-budget. Experimental results on three different touristic cities show that our approach is effective and outperforms strong baselines.
Automatic construction of domain and aspect specific sentiment lexicons for customer review mining
Authors: Jürgen Broß, Heiko Ehrig
Venue: CIKM
Year: 2013

Automatically analyzing the opinions expressed in customer reviews is of high relevance in many application scenarios, e.g., market research, trend analysis, or reputation management. A great share of current sentiment analysis approaches makes use of special purpose lexicons that provide information about the polarity (e.g., positive or negative) of individual words and phrases. One major challenge is that the actual sentiment polarity of a specific expression is often context dependent (e.g., "long+ battery life" vs. "long- flash recycle time"). However, the vast majority of existing approaches focuses on creating general purpose lexicons. Especially in the context of mining customer review data, the use of such lexicons is rather suboptimal as they fail to adequately reflect the domain specific lexical usage. We propose a novel method that allows to automatically adapt and extend existing lexicons to a specific product domain. We follow a corpus-based approach and exploit the fact that many customer reviews exhibit some form of semi-structure. The method is fully automatic and thus scales well across different product domains. Our experiments show that the extracted lexicons are highly accurate and significantly improve the performance in a sentiment classification scenario.
Translating a trillion points of data into therapies, diagnostics, and new insights into disease
Authors: Atul J. Butte
Venue: CIKM
Year: 2013

There is an urgent need to translate genome-era discoveries into clinical utility, but the difficulties in making bench-to-bedside translations have been well described. The nascent field of translational bioinformatics may help. Dr. Butte's lab at Stanford builds and applies tools that convert more than a trillion points of molecular, clinical, and epidemiological data - measured by researchers and clinicians over the past decade - into diagnostics, therapeutics, and new insights into disease. Dr. Butte, a bioinformatician and pediatric endocrinologist, will highlight his lab's work on using publicly-available molecular measurements to find new uses for drugs including drug repositioning for inflammatory bowel disease, discovering new treatable inflammatory mechanisms of disease in type 2 diabetes, and the evaluation of patients presenting with whole genomes sequenced.
DTMBIO 2013: international workshop on data and text mining in biomedical informatics
Authors: Atul J. Butte, Doheon Lee, Hua Xu, Min Song
Venue: CIKM
Year: 2013

The organizers of ACM Seventh International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO 13) are pleased to announce that the seventh DTMBIO will be held in conjunction with CIKM, one of the largest data management conferences. The major interests of DTMBIO are on the state-of-the-art applications of data and text mining on biomedical research problems. DTMBIO 13 will be a forum of discussing and exchanging informatics related techniques and problems in the context of biomedical research.
Bayesian variable selection for linear regression in high dimensional microarray data
Authors: Wellington Cabrera, Carlos Ordonez, David Sergio Matusevich, Veerabhadran Baladandayuthapani
Venue: CIKM
Year: 2013

Variable selection is a fundamental problem in Bayesian statistics whose solution requires exploring a combinatorial search space. We study the solution of variable selection with a well-known MCMC method, which requires thousands of iterations. We present several algorithmic optimizations to accelerate the MCMC method to make it work efficiently inside a database system. Our optimizations include sufficient statistics, variable preselection, hash tables and calling a linear algebra library. We present experiments with very high dimensional microarray data sets to predict cancer survival time. We discuss encouraging findings, identifying specific genes likely to predict the survival time for brain cancer patients. We also show our DBMS-based algorithm is orders of magnitude faster than the R statistical package. Our work shows a DBMS is a promising platform to analyze microarray data.
2013 international workshop on computational scientometrics: theory and applications
Authors: Cornelia Caragea, C. Lee Giles, Lior Rokach, Xiaozhong Liu
Venue: CIKM
Year: 2013

The field of Scientometrics is concerned with the analysis of science and scientific research. As science advances, scientists around the world continue to produce large numbers of research articles, which provide the technological basis for worldwide collection, sharing, and dissemination of scientific discoveries. Research ideas are generally developed based on high quality citations. Understanding how research ideas emerge, evolve, or disappear as a topic, what is a good measure of quality of published works, what are the most promising areas of research, how authors connect and influence each other, who are the experts in a field, what works are similar, and who funds a particular research topic are some of the major foci of the rapidly emerging field of Scientometrics. Digital libraries and other databases that store research articles have become a medium for answering such questions. Citation analysis is used to mine large publication graphs in order to extract patterns in the data (e.g., citations per article) that can help measure the quality of a journal. Scientometrics, on the other hand, is used to mine graphs that link together multiple types of entities: authors, publications, conference venues, journals, institutions, etc., in order to assess the quality of science and answer complex questions such as those listed above. Tools such as maps of science that are built from digital libraries, allow different categories of users to satisfy various needs, e.g., help researchers to easily access research results, identify relevant funding opportunities, and find collaborators. Moreover, the recent developments in data mining, machine learning, natural language processing, and information retrieval makes it possible to transform the way we analyze research publications, funded proposals, patents, etc., on a web-wide scale.
Position-based contextualization for passage retrieval
Authors: David Carmel, Anna Shtok, Oren Kurland
Venue: CIKM
Year: 2013

We present a novel contextualization approach for passage retrieval. The core principle is to let any occurrence of a query term in a document affect the passage retrieval score, whether the occurrence is in the passage or not. This effect is controlled by the distance between the term occurrence and the passage. Empirical evaluation demonstrates the merits of our approach; the resultant retrieval performance substantially transcends that of previously proposed passage retrieval methods, including those that use various contextualization approaches.
Clustering: probably approximately useless?
Authors: Rich Caruana
Venue: CIKM
Year: 2013

Clustering never seems to live up to the hype. To paraphrase the popular saying, clustering looks good in theory, yet often fails to deliver in practice. Why? You would think that something so simple and elegant as finding groups of similar items in data would be incredibly useful. Yet often it isn't. The problem is that clustering rarely finds the groups you want, or expected, or that are most useful for the task at hand. There are so many good ways to cluster a dataset that the odds of coming up with the clustering that is best for what you are doing now are small. How do we fix this and make clustering more useful in practice? How do we make clustering do what you want, while still giving it the freedom to "do its own thing" and surprise us?
Discovering relations using matrix factorization methods
Authors: Ervina Çergani, Pauli Miettinen
Venue: CIKM
Year: 2013

Traditional relation extraction methods work on manually defined relations and typically expect manually labelled extraction patterns for each relation. This strongly limits the scalability of these systems. In Open Relation Extraction (ORE), the relations are identified automatically based on co-occurrences of "surface relations" (contexts) and entity pairs. The recently-proposed methods for ORE use partition clustering to find the relations. In this work we propose the use of matrix factorization methods instead of clustering. Specifically, we study Non-Negative Matrix Factorization (NMF) and Boolean Matrix Factorization (BMF). These methods overcome many problems inherent in clustering and perform better than the k-means clustering in our evaluation.
Probabilistic latent class models for predicting student performance
Authors: Suleyman Cetintas, Luo Si, Yan Ping Xin, Ron Tzur
Venue: CIKM
Year: 2013

Predicting student performance is an important task for many core problems in intelligent tutoring systems. This paper proposes a set of novel probabilistic latent class models for the task. The most effective probabilistic model utilizes all available information about the educational content and users/students to jointly identify hidden classes of students and educational content that share similar characteristics, and to learn a specialized and fine-grained regression model for each latent educational content and student class. Experiments carried out on large-scale real-world datasets demonstrate the advantages of the proposed probabilistic latent class models.
Software plagiarism detection: a graph-based approach
Authors: Dong-Kyu Chae, Jiwoon Ha, Sang-Wook Kim, Boojoong Kang, Eul Gyu Im
Venue: CIKM
Year: 2013

As plagiarism of software increases rapidly, there are growing needs for software plagiarism detection systems. In this paper, we propose a software plagiarism detection system using an API-labeled control flow graph (A-CFG) that abstracts the functionalities of a program. The A-CFG can reflect both the sequence and the frequency of APIs, while previous work rarely considers both of them together. To perform a scalable comparison of a pair of A-CFGs, we use random walk with restart (RWR) that computes an importance score for each node in a graph. By the RWR, we can generate a single score vector for an A-CFG and can also compare A-CFGs by comparing their score vectors. Extensive evaluations on a set of Windows applications demonstrate the effectiveness and the scalability of our proposed system compared with existing methods.
Discovering latent blockmodels in sparse and noisy graphs using non-negative matrix factorisation
Authors: Jeffrey Chan, Wei Liu, Andrey Kan, Christopher Leckie, James Bailey, Kotagiri Ramamohanarao
Venue: CIKM
Year: 2013

Blockmodelling is an important technique in social network analysis for discovering the latent structure in graphs. A blockmodel partitions the set of vertices in a graph into groups, where there are either many edges or few edges between any two groups. For example, in the reply graph of a question and answer forum, blockmodelling can identify the group of experts by their many replies to questioners, and the group of questioners by their lack of replies among themselves but many replies from experts.
PredictionIO: a distributed machine learning server for practical software development
Authors: Simon Chan, Thomas Stone, Kit Pang Szeto, Ka-Hou Chan
Venue: CIKM
Year: 2013

One of the biggest challenges for software developers to build real-world predictive applications with machine learning is the steep learning curve of data processing frameworks, learning algorithms and scalable system infrastructure. We present PredictionIO, an open source machine learning server that comes with a step-by-step graphical user interface for developers to (i) evaluate, compare and deploy scalable learning algorithms, (ii) tune hyperparameters of algorithms manually or automatically and (iii) evaluate model training status. The system also comes with an Application Programming Interface (API) to communicate with software applications for data collection and prediction retrieval. The whole infrastructure of PredictionIO is horizontally scalable with a distributed computing component based on Hadoop. The demonstration shows a live example and workflows of building real-world predictive applications with the graphical user interface of PredictionIO, from data collection, algorithm tuning and selection, model training and re-training to real-time prediction querying.
Community question topic categorization via hierarchical kernelized classification
Authors: Wen Chan, Weidong Yang, Jinhui Tang, Jintao Du, Xiangdong Zhou, Wei Wang
Venue: CIKM
Year: 2013

We present a hierarchical kernelized classification model for the automatic classification of general questions into their corresponding topic categories in community Question Answering service (cQAs). This could save many efforts of manual classification and facilitate browsing as well as better retrieving of questions from the cQA archives. To deal with the challenge of short text message of questions, we explore and optimally combine various cQA features by introducing multiple kernel learning strategy into the hierarchical classification framework. We propose a hybrid regularization approach of combining orthogonal constraint and L1 sparseness in our framework to promote the discriminative power on similar topics as well as sparsing the model parameters. The experimental results on a real world dataset from Yahoo! Answers demonstrate the effectiveness of our proposed model as compared to the state-of-the-art methods and strong baselines.
Nonparametric bayesian multitask collaborative filtering
Authors: Sotirios Chatzis
Venue: CIKM
Year: 2013

Paper presented at 22nd ACM International Conference on Information & Knowledge Management, 2013, San Francisco, CA, USA, 27 October - 1 November
An efficient algorithm for approximate betweenness centrality computation
Authors: Mostafa Haghir Chehreghani
Venue: CIKM
Year: 2013

Betweenness centrality is an important centrality measure widely used in social network analysis, route planning etc. However, even for mid-size networks, it is practically intractable to compute exact betweenness scores. In this paper, we propose a generic randomized framework for unbiased approximation of betweenness centrality. The proposed framework can be adapted with different sampling techniques and give diverse methods. We discuss the conditions a promising sampling technique should satisfy to minimize the approximation error and present a sampling method partially satisfying the conditions. We perform extensive experiments and show the high efficiency and accuracy of the proposed method.
Spatial-temporal query homogeneity for KNN object search on road networks
Authors: Ying-Ju Chen, Kun-Ta Chuang, Ming-Syan Chen
Venue: CIKM
Year: 2013

We in this paper explore a new research paradigm, called query homogeneity, to process KNN queries on road networks for online LBS applications. While previous works in the literature concentrate on the improvement of query processing time, we turn to examine the issue of response time for a user query, which needs to additionally consider the waiting time in the queue. Note that the response time is the more precise value corresponding to the user experience in an online service, and the unacceptable response time is likely to turn away disgruntled users. Surprisingly, we will show in this paper that the response time will be more significantly dominated by the waiting time but it is left unexplored thus far. Since previous works all perform queries in the one-by-one fashion, which will lead to unexpected long waiting time, we thus in this paper propose a novel query framework, called SHI, aiming at diminishing the waiting time by a new group-by-group solution. SHI relies on the natural phenomenon of query homogeneity, which refers to the behavior that queries are usually issued in the sense of spatial and temporal correlation. Motivated by this natural behavior, operations of query processing and queue processing are incorporated in the SHI framework. During the network expansion for a query, a group of homogeneity queries in the waiting queue, which have results identical to the processing query, will be picked up and flushed out together when the query processing is accomplished, achieving the group-by-group query processing and reducing the waiting time significantly.
Efficient pruning algorithm for top-K ranking on dataset with value uncertainty
Authors: Jianwen Chen, Ling Feng
Venue: CIKM
Year: 2013

Top-K ranking query in uncertain databases aims to find the top-K tuples according to a ranking function. The interplay between score and uncertainty makes top-K ranking in uncertain databases an intriguing issue, leading to rich query semantics. Recently, a unified ranking framework based on parameterized ranking functions (PRFs) is formulated, which generalizes many previously proposed ranking semantics. Under the PRFs based ranking framework, efficient pruning approach for Top-K ranking on dataset with tuple uncertainty has been well studied in the literature. However, this cannot be applied to top-K ranking on dataset with value uncertainty (described through attribute-level uncertain data model), which are often natural and useful in analyzing uncertain data in many applications. This paper aims to develop efficient pruning techniques for top-K ranking on dataset with value uncertainty under the PRFs based ranking framework, which has not been well studied in the literature. We present the mathematics of deriving the pruning techniques and the corresponding algorithms. The experimental results on both real and synthetic data demonstrate the effectiveness and efficiency of the proposed pruning techniques.
Cost-sensitive learning for large-scale hierarchical classification
Authors: Jianfu Chen, David Scott Warren
Venue: CIKM
Year: 2013

We study hierarchical classification of products in electronic commerce, classifying a text description of a product into one of the leaf classes of a tree-structure taxonomy. In particular, we investigate two essential problems, performance evaluation and learning, in a synergistic way. Unless we know what is the appropriate performance evaluation metric for a task, we are not going to learn a classifier of maximum utility for the task. Given the characteristics of the task of hierarchical product classification, we shed insight into how and why common evaluation metrics such as error rate can be misleading, which is applicable for treating other real world applications. The analysis leads to a new performance evaluation metric that tailors this task to reflect a vendor's business goal of maximizing revenue. The proposed metric has an intuitive meaning as the average revenue loss, which depends on both the value of individual products and the hierarchical distance between the true class and the predicted class. Correspondingly, our learning algorithm uses multi-class SVM with margin re-scaling to optimize the proposed metric, instead of error rate or other common metrics. Margin re-scaling is sensitive to the scaling of loss functions. We propose a loss normalization approach to appropriately calibrating the scaling of loss functions, which is applicable to general classification and structured prediction tasks whenever using structured SVM with margin re-scaling. Experiments on a large dataset show that our approach outperforms standard multi-class SVM in terms of the proposed metric, effectively reducing the average revenue loss.
Feedback-driven multiclass active learning for data streams
Authors: Yu Cheng, Zhengzhang Chen, Lu Liu, Jiang Wang, Ankit Agrawal, Alok N. Choudhary
Venue: CIKM
Year: 2013

Active learning is a promising way to efficiently build up training sets with minimal supervision. Most existing methods consider the learning problem in a pool-based setting. However, in a lot of real-world learning tasks, such as crowdsourcing, the unlabeled samples, arrive sequentially in the form of continuous rapid streams. Thus, preparing a pool of unlabeled data for active learning is impractical. Moreover, performing exhaustive search in a data pool is expensive, and therefore unsuitable for supporting on-the-fly interactive learning in large scale data. In this paper, we present a systematic framework for stream-based multi-class active learning. Following the reinforcement learning framework, we propose a feedback-driven active learning approach by adaptively combining different criteria in a time-varying manner. Our method is able to balance exploration and exploitation during the learning process. Extensive evaluation on various benchmark and real-world datasets demonstrates the superiority of our framework over existing methods.
Bootstrapping active name disambiguation with crowdsourcing
Authors: Yu Cheng, Zhengzhang Chen, Jiang Wang, Ankit Agrawal, Alok N. Choudhary
Venue: CIKM
Year: 2013

Name disambiguation is a challenging and important problem in many domains, such as digital libraries, social media management and people search systems. Traditional methods, based on direct assignment using supervised machine learning techniques, seem to be the most effective, but their performances are highly dependent on the amount of training data, while large data annotation can be expensive and time-consuming requiring hours of manual inspection by a domain expert. To efficiently acquire labeled data, we propose a bootstrapping algorithm for the name disambiguation task based on active learning and crowdsourced labeling. We show that the proposed method can leverage the advantages of exploration and exploitation by combining two strategies, thereby improving the overall quality of the training data at minimal expense. The experimental results on two datasets DBLP and ArnetMiner demonstrate the superiority of our framework over existing methods.