OpenRaiser/Intern-Atlas huggingface dataset:
Discriminative feature selection for multi-view cross-domain learning
Authors: Zheng Fang, Zhongfei (Mark) Zhang
Venue: CIKM
Year: 2013

In many data mining applications, we often face the problem of cross-domain learning, i.e., to transfer the already learned knowledge from a source domain to a target domain. In particular, this problem becomes very challenging when there is no or little labeled training data available in the target domain, which is not an uncommon scenario as it is expensive and in certain cases even impossible to obtain any labeled training data in the target domain in many real world applications. In the literature, though few efforts are reported to attempt to solve this challenging problem, the solutions are all rather limited making this problem still open and challenging. On the other hand, as it is not uncommon to face this problem in many applications, an effective solution to this problem shall generate substantial societal impacts. In this paper, we address this problem and propose a new framework, called DISMUTE, taking advantage of the typically available multiple views of the data in domains. Consequently, DISMUTE is based on discriminative feature selection for multi-view cross-domain learning. Theoretic analysis and extensive evaluations in the specific application of object identification and image classification against several state-of-the-art methods demonstrate the outstanding superiority of DISMUTE.
Leveraging data to change industry paradigms
Authors: Chris Farmer
Venue: CIKM
Year: 2013

Much of the conversation on "big data" is centered on data technologies and analytics platforms and how established companies apply them. While those technologies and platforms are certainly very important for industry incumbents, data analytics is also often a key building block for new start-up entrants looking to disrupt industry verticals. In many cases, the best examples of novel applications of data to create new services and competitive advantage require a complete rethinking of organizational design in order to create feedback loops and rethink cost structures. The company I founded, SignalFire is applying data for competitive advantage in my own industry, venture capital, but there are myriad examples of this trend across industries such as transportation, financial services, retail, media and many other markets. In this talk, I will discuss how we analyze these trends as venture capitalists and will look at a few case studies of specific companies leveraging data to innovate in their industries.
Recommendation via user's personality and social contextual
Authors: He Feng, Xueming Qian
Venue: CIKM
Year: 2013

With the advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like interpersonal influence and interest based on circles of friends bring opportunities and challenges for recommender system (RS) to solve the cold start and sparsity problem of datasets. Some of the social factors have been used in RS, but have not been fully considered. In this paper, three social factors, personal interest, interpersonal interest similarity and interpersonal influence, fuse into a unified personalized recommendation model based on probabilistic matrix factorization. The factor of personal interest can make the RS recommend items to meet users' individualities, especially for experienced users. Moreover, for cold start users, the interpersonal interest similarity and interpersonal influence can enhance the intrinsic link among features in the latent space. We conduct a series of experiments on real rating datasets. Experimental results show the proposed approach outperforms the existing RS approaches .
Spatial search for K diverse-near neighbors
Authors: Gregory Ference, Wang-Chien Lee, Hui-Ju Hung, De-Nian Yang
Venue: CIKM
Year: 2013

To many location-based service applications that prefer diverse results, finding locations that are spatially diverse and close in proximity to a query point (e.g., the current location of a user) can be more useful than finding the k nearest neighbors/locations. In this paper, we investigate the problem of searching for the k Diverse-Near Neighbors (kDNNs)} in spatial space that is based upon the spatial diversity and proximity of candidate locations to the query point. While employing a conventional distance measure for proximity, we develop a new and intuitive diversity metric based upon the variance of the angles among the candidate locations with respect to the query point. Accordingly, we create a dynamic programming algorithm that finds the optimal kDNNs. Unfortunately, the dynamic programming algorithm, with a time complexity of O(kn3), incurs excessive computational cost. Therefore, we further propose two heuristic algorithms, namely, Distance-based Browsing (DistBrow) and Diversity-based Browsing (DivBrow) that provide high effectiveness while being efficient by exploring the search space prioritized upon the proximity to the query point and spatial diversity, respectively. Using real and synthetic datasets, we conduct a comprehensive performance evaluation. The results show that DistBrow and DivBrow have superior effectiveness compared to state-of-the-art algorithms while maintaining high efficiency.
Location recommendation for out-of-town users in location-based social networks
Authors: Gregory Ference, Mao Ye, Wang-Chien Lee
Venue: CIKM
Year: 2013

Most previous research on location recommendation services in location-based social networks (LBSNs) makes recommendations without considering where the targeted user is currently located. Such services may recommend a place near her hometown even if the user is traveling out of town. In this paper, we study the issues in making location recommendations for out-of-town users by taking into account user preference, social influence and geographical proximity. Accordingly, we propose a collaborative recommendation framework, called User Preference, Proximity and Social-Based Collaborative Filtering} (UPS-CF), to make location recommendation for mobile users in LBSNs. We validate our ideas by comprehensive experiments using real datasets collected from Foursquare and Gowalla. By comparing baseline algorithms and conventional collaborative filtering approach (and its variants), we show that UPS-CF exhibits the best performance. Additionally, we find that preference derived from similar users is important for in-town users while social influence becomes more important for out-of-town users.
RWS-Diff: flexible and efficient change detection in hierarchical data
Authors: Jan P. Finis, Martin Raiber, Nikolaus Augsten, Robert Brunel, Alfons Kemper, Franz Färber
Venue: CIKM
Year: 2013

The problem of generating a cost-minimal edit script between two trees has many important applications. However, finding such a cost-minimal script is computationally hard, thus the only methods that scale are approximate ones. Various approximate solutions have been proposed recently. However, most of them still show quadratic or worse runtime complexity in the tree size and thus do not scale well either. The only solutions with log-linear runtime complexity use simple matching algorithms that only find corresponding subtrees as long as these subtrees are equal. Consequently, such solutions are not robust at all, since small changes in the leaves which occur frequently can make all subtrees that contain the changed leaves unequal and thus prevent the matching of large portions of the trees. This problem could be avoided by searching for similar instead of equal subtrees but current similarity approaches are too costly and thus also show quadratic complexity. Hence, currently no robust log-linear method exists.
Timeline adaptation for text classification
Authors: Fumiyo Fukumoto, Yoshimi Suzuki, Atsuhiro Takasu
Venue: CIKM
Year: 2013

In this paper, we address the text classification problem that a period of time created test data is different from the training data, and present a method for text classification based on temporal adaptation. We first applied lexical chains for the training data to collect terms with semantic relatedness, and created sets (we call these Sem sets). Semantically related terms in the documents are replaced to their representative term. For the results, we identified short terms that are salient for a specific period of time. Finally, we trained SVM classifiers by applying a temporal weighting function to each selected short terms within the training data, and classified test data. Temporal weighting function is weighted each short term in the training data according to the temporal distance between training and test data. The results using MedLine data showed that the method was comparable to the current state-of-the-art biased-SVM method, especially the method is effective when testing on data far from the training data.
One size does not fit all: multi-granularity search of web forums
Authors: Gayatree Ganu, Amélie Marian
Venue: CIKM
Year: 2013

Users rely increasingly on online forums, blogs, and mailing lists to exchange information, practical tips, and stories. Although this type of social interaction has become central to our daily lives and decision-making processes, forums are surprisingly technologically poor: often there is no choice but to browse through massive numbers of posts while looking for specific information. A critical challenge then for forum search is to provide results that are as complete as possible and that do not miss some relevant information but that are not too broad. In this paper, we address the problem of presenting textual search results in a concise manner to answer user needs. Specifically, we propose a new search approach over free-form text in forums that allows for the search results to be returned at varying granularity levels. We implement a novel hierarchical representation and scoring technique for objects at multiple granularities, taking into account the inherent containment relationship provided by the hierarchy. We also present a score optimization algorithm that efficiently chooses the best k-sized result set while ensuring no overlap between the results. We evaluate the effectiveness of multi-granularity search by conducting extensive user studies and show that a mixed granularity set of results is more relevant to users than standard post-only approaches.
Joint learning on sentiment and emotion classification
Authors: Wei Gao, Shoushan Li, Sophia Yat Mei Lee, Guodong Zhou, Chu-Ren Huang
Venue: CIKM
Year: 2013

Sentiment and emotion classification have been popularly but separately studied in natural language processing. In this paper, we address joint learning on sentiment and emotion classification where both the labeled data for sentiment and emotion classification are available. The objective of this joint-learning is to benefit the two tasks from each other for improving their performances. Specifically, an extra data set that is annotated with both sentiment and emotion labels are employed to estimate the transformation probability between the two kinds of labels. Furthermore, the transformation probability is leveraged to transfer the classification labels to benefit the two tasks from each other. Empirical studies demonstrate the effectiveness of our approach for the novel joint learning task.
Learning user's intent using user tags: intelligent interactive image search system
Authors: Viktors Garkavijs
Venue: CIKM
Year: 2013

The contents of the image files can be completely different per se, if we consider the descriptors such as color histograms, textures, edge histograms, etc., however they might have the same semantic metadata, such as user tags, at the same time. The exploitation of user tags in image search tasks results in lower computational costs considering much smaller number of features that are necessary to compute the similarity between the objects, as well as it helps to overcome the semantic gap (this kind of similarity is, of course, different from the one computed in content-based methods). Thanks to these two properties it becomes very easy to create an interactive image search system that is based on online learning from interaction between the user and the system. We propose a simple algorithm for system training, that uses dwell-time data as input parameters for relevance recalculation, which we implemented in the prototype of our experimental search system.
OMS-TL: a framework of online multiple source transfer learning
Authors: Liang Ge, Jing Gao, Aidong Zhang
Venue: CIKM
Year: 2013

Transfer learning has benefitted many real-world applications where labeled data are abundant in source domains but scarce on the target domain. As there are usually multiple relevant domains where knowledge can be transferred, Multiple Source Transfer Learning (MSTL) has recently attracted much attention. Most existing MSTL methods work in an offline fashion in that they have to store all the data on the target domain before learning. However, in some time-critical applications where the data arrive sequentially in large volume, a fast and scalable online method that can transfer knowledge from multiple source domains is much needed. To achieve this end, in this paper, we propose a new framework of Online Multiple Source Transfer Learning (OMS-TL). The framework is based on a convex optimization problem where knowledge transferred from multiple source domains are guided by the information on the target domain. The proposed method is fast, scalable and enjoys the theoretical guarantees of standard online algorithms. Extensive experiments are conducted on three real-life data sets. The results show that the performance of OMS-TL is close to that of its offline counterpart, which bears comparable performance to existing baseline methods. Furthermore, the proposed method has great scalability and fast response time.
Exploiting collaborative filtering techniques for automatic assessment of student free-text responses
Authors: Tao Ge, Zhifang Sui, Baobao Chang
Venue: CIKM
Year: 2013

The automatic assessment of free-text responses of students is a relatively newer task in both computational linguistics and educational technology. The goal of the task is to produce an assessment of student answers to explanation and definition questions typically asked in problems seen in practice exercises or tests. Unlike some conventional methods which assess the student responses based on only information about their corresponding questions, this paper exploits idea of collaborative filtering to analyze student responses and used an effective collaborative filtering model -- feature-based matrix factorization model to deal with this challenge. The experimental results show that our feature-based matrix factorization model outperforms the baseline models and the model with a re-ranking phase can achieve a better and competitive performance -- 63.6% overall accuracy on the Beetle dataset.
On sampling the wisdom of crowds: random vs. expert sampling of the twitter stream
Authors: Saptarshi Ghosh, Muhammad Bilal Zafar, Parantapa Bhattacharya, Naveen Kumar Sharma, Niloy Ganguly, P. Krishna Gummadi
Venue: CIKM
Year: 2013

Several applications today rely upon content streams crowd-sourced from online social networks. Since real-time processing of large amounts of data generated on these sites is difficult, analytics companies and researchers are increasingly resorting to sampling. In this paper, we investigate the crucial question of how to sample the data generated by users in social networks. The traditional method is to randomly sample all the data. We analyze a different sampling methodology, where content is gathered only from a relatively small subset (< 1%) of the user population namely, the expert users. Over the duration of a month, we gathered tweets from over 500,000 Twitter users who are identified as experts on a diverse set of topics, and compared the resulting expert-sampled tweets with the 1% randomly sampled tweets provided publicly by Twitter. We compared the sampled datasets along several dimensions, including the diversity, timeliness, and trustworthiness of the information contained within them, and find important differences between the datasets. Our observations have major implications for applications such as topical search, trustworthy content recommendations, and breaking news detection.
Scholarly big data: information extraction and data mining
Authors: C. Lee Giles
Venue: CIKM
Year: 2013

Collections of scholarly documents are usually not thought of as big data. However, large collections of scholarly documents often have many millions of publications, authors, citations, equations, figures, etc., and large scale related data and structures such as social networks, slides, data sets, etc. We discuss scholarly big data challenges, insights, methodologies and applications. We illustrate scholarly big data issues with examples of specialized search engines and recommendation systems that use information extraction and data mining in various areas such as computer science, chemistry, archaeology, acknowledgements, reference recommendation, collaboration recommendation, and others.
Efficient hierarchical clustering of large high dimensional datasets
Authors: Sean Gilpin, Buyue Qian, Ian Davidson
Venue: CIKM
Year: 2013

Hierarchical clustering is extensively used to organize high dimensional objects such as documents and images into a structure which can then be used in a multitude of ways. However, existing algorithms are limited in their application since the time complexity of agglomerative style algorithms can be as much as O(n2log n) where n is the number of objects. Furthermore the computation of similarity between such objects is itself time consuming given they are high dimension and even optimized built in functions found in MATLAB take the best part of a day to handle collections of just 10,000 objects on typical machines. In this paper we explore using angular hashing to hash objects with similar angular distance to the same hash bucket. This allows us to create hierarchies of objects within each hash bucket and to hierarchically cluster the hash buckets themselves. With our formal guarantees on the similarity of objects in the same bucket this leads to an elegant agglomerative algorithm with strong performance bounds. Our experimental results show that not only is our approach thousands of times faster than regular agglomerative algorithms but surprisingly the accuracy of our results is typically as good and can sometimes be substantially better.
Transferring knowledge with source selection to learn IR functions on unlabeled collections
Authors: Parantapa Goswami, Massih-Reza Amini, Éric Gaussier
Venue: CIKM
Year: 2013

We investigate the problem of learning an IR function on a collection without relevance judgements (called target collection) by transferring knowledge from a selected source collection with relevance judgements. To do so, we first construct, for each query in the target collection, relative relevance judgment pairs using information from the source collection closest to the query (selection and transfer steps), and then learn an IR function from the obtained pairs in the target collection (self-learning step). For the transfer step, the relevance information in the source collection is summarized as a grid that provides, for each term frequency and document frequency values of a word in a document, an empirical estimate of the relevance of the document. The self-learning step iteratively assigns pairwise preferences to documents in the target collection using the scores of the former learned function. We show the effectiveness of our approach through a series of extensive experiments on CLEF and several collections from TREC used either as target or source datasets. Our experiments show the importance of selecting the source collection prior to transfer information to the target collection, and demonstrate that the proposed approach yields results consistently and significantly above state-of-the-art IR functions.
ESTHETE: a news browsing system to visualize the context and evolution of news stories
Authors: Rahul Goyal, Ravee Malla, Amitabha Bagchi, Sameep Mehta, Maya Ramanath
Venue: CIKM
Year: 2013

Providing the history and context(s) of a news article that emerges in the middle of an evolving news story--sometimes multiple news stories--is a complex task. The complexity of the task is compounded by the fact that different users are interested in different contexts of the article, and it is impossible to guess what a particular user is most interested in. In this paper, we introduce ESTHETE, a system that provides rich context(s) (through what we call personalized flexible context extraction), by preprocessing and storing articles in a structured representation (directed graphs) that makes it easy for the user to explore different contexts. The advantage of this approach is that the incremental computational expense in incorporating new articles as they are published is minimal. Our system is available at: http://konfrap.com/esthete.
AnchorMF: towards effective event context identification
Authors: Hansu Gu, Mike Gartrell, Liang Zhang, Qin Lv, Dirk Grunwald
Venue: CIKM
Year: 2013

Online social networks (OSNs) such as Twitter provide a good platform for event discussions. Recent research [26][25] as shown that event discussions in OSNs are diverse and innovative and encourage public engagement in events. Although much research has been conducted in OSNs to track and detect events, there has been limited research on detecting or understanding the event context. Event context helps to better predict users' participation in events, identify relations among events, and recommend friends who share similar event context.
READFAST: high-relevance search-engine for big text
Authors: Michael N. Gubanov, Anna Pyayt
Venue: CIKM
Year: 2013

Relevance of search-results is a key factor for any search engine. In order to return and rank the Web-pages that are most relevant to the query, contemporary search engines use complex ranking functions that depend on hundreds of features. For example, presence or absence of the query keywords on the page, their proximity, frequencies, HTML markup are just a few to name. Additional features might include fonts, tags, hyperlinks, metadata, and parts of the Web-page description. All this information is used by the search-engine to rank HTML Web pages returned to the user, but is unfortunately absent in free text that has no HTML markup, tags, hyperlinks, and any other metadata, except implicit natural language structure.
Usability in machine learning at scale with graphlab
Authors: Carlos Guestrin
Venue: CIKM
Year: 2013

Today, machine learning (ML) methods play a central role in industry and science. The growth of the Web and improvements in sensor data collection technology have been rapidly increasing the magnitude and complexity of the ML tasks we must solve. This growth is driving the need for scalable, parallel ML algorithms that can handle "Big Data."
Course specific search engines: a study in incorporating context into search
Authors: Neel Guha
Venue: CIKM
Year: 2013

short-paper Share on Course specific search engines: a study in incorporating context into search Author: Neel Guha Henry M. Gunn High School, Palo Alto, CA, USA Henry M. Gunn High School, Palo Alto, CA, USAView Profile Authors Info & Claims ESAIR '13: Proceedings of the sixth international workshop on Exploiting semantic annotations in information retrievalOctober 2013 Pages 33–36https://doi.org/10.1145/2513204.2513216Published:28 October 2013Publication History 0citation84DownloadsMetricsTotal Citations0Total Downloads84Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access