OpenRaiser/Intern-Atlas huggingface dataset:
Predicting aggregate social activities using continuous-time stochastic process
Authors: Shu Huang, Min Chen, Bo Luo, Dongwon Lee
Venue: CIKM
Year: 2012

How to accurately model and predict the future status of social networks has become an important problem in recent years. Conventional solutions to such a problem often employ topological structure of the sociogram, i.e., friendship links. However, they often disregard different levels of activeness of social actors and become insufficient to deal with complex dynamics of user behaviors. In this paper, to address this issue, we first refine the notion of social activity to better describe dynamic user behaviors in social networks. We then propose a Parameterized Social Activity Model (PSAM) using continuous-time stochastic process for predicting aggregate social activities. With social activities evolving over time, PSAM itself also evolves and therefore dynamically captures the real-time characteristics of the current active population. Our experiments using two real social networks (Facebook and CiteSeer) reveal that the proposed PSAM model is effective in simulating social activity evolution and predicting aggregate social activities accurately at different time scales.
Efficient safe-region construction for moving top-K spatial keyword queries
Authors: Weihuang Huang, Guoliang Li, Kian-Lee Tan, Jianhua Feng
Venue: CIKM
Year: 2012

Many real-world applications have requirements to support moving spatial keyword queries. For example a tourist looks for top-k "seafood restaurants" while walking in a city. She will continuously issue moving queries. However existing spatial keyword search methods focus on static queries and it calls for new effective techniques to support moving queries efficiently. In this paper we propose an effective method to support moving top-k spatial keyword queries. In addition to finding top-k answers of a moving query, we also calculate a safe region such that if a new query with a location falling in the safe region, we can directly use the answer set to answer the query. To this end, we propose an effective model to represent the safe region and devise efficient search algorithms to compute the safe region. We have implemented our method and experimental results on real datasets show that our method achieves high efficiency and outperforms existing methods significantly.
Local anomaly descriptor: a robust unsupervised algorithm for anomaly detection based on diffusion space
Authors: Hao Huang, Hong Qin, Shinjae Yoo, Dantong Yu
Venue: CIKM
Year: 2012

Current popular anomaly detection algorithms are capable of detecting global anomalies but oftentimes fail to distinguish local anomalies from normal instances. This paper aims to improve unsupervised anomaly detection via the exploration of physics-based diffusion space. Building upon the embedding manifold derived from diffusion maps, we devise Local Anomaly Descriptor (LAD) whose originality results from faithfully preserving intrinsic and informative density-relevant neighborhood information. This robust and effective algorithm is designed with a weighted umbrella Laplacian operator to bridge global and local properties. To further enhance the efficacy of our proposed algorithm, we explore the utility of anisotropic Gaussian kernel (AGK) which can offer better manifold-aware affinity information. Comprehensive experiments on both synthetic and UCI real datasets verify that our LAD outperforms existing anomaly detection algorithms.
On using category experts for improving the performance and accuracy in recommender systems
Authors: Won-Seok Hwang, Ho-Jong Lee, Sang-Wook Kim, Minsoo Lee
Venue: CIKM
Year: 2012

A variety of recommendation methods have been proposed to satisfy the performance and accuracy; however, it is fairly difficult to satisfy both of them because there is a trade-off between them. In this paper, we introduce the notion of category experts and propose the recommendation method by exploiting the ratings of category experts instead of those of the users similar to a target user. We also extend the method that uses both the category preference of a target user and his/her similarity to category experts. We show that our method significantly outperforms the existing methods in terms of performance and accuracy through extensive experiments with real-world data.
A probabilistic approach to mining geospatial knowledge from social annotations
Authors: Suradej Intagorn, Kristina Lerman
Venue: CIKM
Year: 2012

User-generated content, such as photos and videos, is often annotated by users with free-text labels, called tags. Increasingly, such content is also georeferenced, i.e., it is associated with geographic coordinates. The implicit relationships between tags and their locations can tell us much about how people conceptualize places and relations between them. However, extracting such knowledge from social annotations presents many challenges, since annotations are often ambiguous, noisy, uncertain and spatially inhomogeneous. We introduce a probabilistic framework for modeling georeferenced annotations and a method for learning model parameters from data. The framework is flexible and general, and can be used in a variety of applications that mine geospatial knowledge from user-generated content. Specifically, we study three problems: extracting place semantics, predicting locations of photos and learning part-of relations between places. We show our method performs well compared to state-of-the-art approaches developed for the first two problems, and offers a novel solution to the problem of learning relations between places.
Is wikipedia too difficult?: comparative analysis of readability of wikipedia, simple wikipedia and britannica
Authors: Adam Jatowt, Katsumi Tanaka
Venue: CIKM
Year: 2012

Readability is one of key factors determining document quality and reader's satisfaction. In this paper we analyze readability of Wikipedia, which is a popular source of information for searchers about unknown topics. Although Wikipedia articles are frequently listed by search engines on top ranks, they are often too difficult for average readers searching information about difficult queries. We examine the average readability of content in Wikipedia and compare it to the one in Simple Wikipedia and Britannica. Next, we investigate readability of selected categories in Wikipedia. Apart from standard readability measures we use some new metrics based on words' popularity and their distributions across different document genres and topics.
Non-stationary bayesian networks based on perfect simulation
Authors: Yi Jia, Wenrong Zeng, Jun Huan
Venue: CIKM
Year: 2012

Non-stationary Dynamic Bayesian Networks (Non-stationary DBNs) are widely used to model the temporal changes of directed dependency structures from multivariate time series data. However, the existing change-points based non-stationary DBNs methods have several drawbacks including excessive computational cost, and low convergence speed. In this paper we proposed a novel non-stationary DBNs method. Our method is based on the perfect simulation model. We applied this approach for network structure inference from synthetic data and biological microarray gene expression data and compared it with other two state-of-the-art non-stationary DBNs methods. The experimental results demonstrated that our method outperformed two other state-of-the-art methods in both computational cost and structure prediction accuracy. The further sensitivity analysis showed that once converged our model is robust to large parameter ranges, which reduces the uncertainty of the model behavior.
Harmonization of detailed clinical models with clinical study data standard
Authors: Guoqian Jiang, Julie Evans, Thomas A. Oniki, Joey Coyle, Landen Bain, Stanley M. Huff, Rebecca Daniels Kush, Christopher G. Chute
Venue: CIKM
Year: 2012

Data sharing and integration between clinical research data management system (CDMS) and electronic health record (EHR) system remains a challenging issue. To deal with the challenge, there is emerging interest in utilizing the Detailed Clinical Modeling (DCM) approach across a variety of contexts. The Intermountain Healthcare Clinical Element Models (CEMs) have been adopted by the Office of the National Coordinator (ONC) awarded SHARPn project for normalizing patient data from the electronic medical records (EMRs). The objective of the present study is to describe our preliminary efforts on harmonization of the SHARPn CEMs with CDISC (Clinical Data Interchange Standards Consortium) clinical study data standards. We were focused on three generic domains: Demographics, Lab Tests and Medications. We performed a panel review on each data element extracted from the CDISC templates and SHARPn CEMs. We have identified a set of data elements that are common to the context of both clinical research and secondary use and discussed outstanding harmonization issues. We consider that the outcomes would be useful for defining new requirements for the DCM modeling community and ultimately facilitating the semantic interoperability between systems for both clinical research and secondary use.
Contextual evaluation of query reformulations in a search session by user simulation
Authors: Jiepu Jiang, Daqing He, Shuguang Han, Zhen Yue, Chaoqun Ni
Venue: CIKM
Year: 2012

We propose a method to dynamically estimate the utility of documents in a search session by modeling the users' browsing behaviors and novelty. The method can be applied to evaluate query reformulations in a search session.
Towards an effective and unbiased ranking of scientific literature through mutual reinforcement
Authors: Xiaorui Jiang, Xiaoping Sun, Hai Zhuge
Venue: CIKM
Year: 2012

It is important to help researchers find valuable scientific papers from a large literature collection containing information of authors, papers and venues. Graph-based algorithms have been proposed to rank papers based on networks formed by citation and co-author relationships. This paper proposes a new graph-based ranking framework MutualRank that integrates mutual reinforcement relationships among networks of papers, researchers and venues to achieve a more synthetic, accurate and fair ranking result than previous graph-based methods. MutualRank leverages the network structure information among papers, authors, and their venues available from a literature collection dataset and sets up a unified mutual reinforcement model that involves both intra- and inter-network information for ranking papers, authors and venues simultaneously. To evaluate, we collect a set of recommended papers from websites of graduate-level computational linguistics courses of 15 top universities as the benchmark and apply different methods to estimate paper importance. The results show that MutualRank greatly outperforms the competitors including Pag-eRank, HITS and CoRank in ranking papers as well as researchers. The experimental results also demonstrate that venues ranked by MutualRank are reasonable.
G-WSTD: a framework for geographic web search topic discovery
Authors: Di Jiang, Jan Vosecky, Kenneth Wai-Ting Leung, Wilfred Ng
Venue: CIKM
Year: 2012

Search engine query log is an important information source that contains millions of users' interests and information needs. In this paper, we tackle the problem of discovering latent geographic search topics via mining search engine query logs. A novel framework G-WSTD that contains search session derivation, geographic information extraction and geographic search topic discovery is developed to support a variety of downstream web applications. The core components of the framework are two topic models, which discover geographic search topics from two different perspectives. The first one is the Discrete Search Topic Model (DSTM), which aims to capture the semantic commonalities across discrete geographic locations. The second one is the Regional Search Topic Model (RSTM), which focuses on a specific region on the map and discovers web search topics that demonstrate geographic locality. We evaluate our framework against several strong baselines on a real-life query log. The framework demonstrates improved data interpretability, better prediction performance and higher topic distinctiveness in the experimentation. The effectiveness of the framework is also verified by applications such as user profiling and URL annotation.
User activity profiling with multi-layer analysis
Authors: Hongxia Jin
Venue: CIKM
Year: 2012

In this paper, we are interested in discovering semantically meaningful communities from a single user's perspective. We define a multi-layer analysis problem to derive a user's activity profile. Such an activity profile would include what activity areas a user is involved with, how important each activity is to the user, and who else is involved with the user on each activity as well as each participant's participation level. We believe a semantically meaningful community (corresponding to an activity area) must also consider the topics of the social messages rather than only the social links. While it is possible to use a hybrid approach based on traditional topic modeling, in this paper we propose a unified user modeling approach based on direct clustering over the social messages taking into considerations of both social connections and topics of social messages. Our clustering algorithm can be performed in a unified way in a unsupervised fashion as well as semi-supervised fashion when the user wants to give our algorithm some seeding inputs on his viewpoints. Moreover, when the new data comes, our algorithm can perform incremental updates on the new data without re-clustering the old data. Our experiments on social media datasets available from both within an enterprise and public social network demonstrate the effectiveness of our approach.
A filter-based protocol for continuous queries over imprecise location data
Authors: Yifan Jin, Reynold Cheng, Ben Kao, Kam-yiu Lam, Yinuo Zhang
Venue: CIKM
Year: 2012

In typical location-based services (LBS), moving objects (e.g., GPS-enabled mobile phones) report their locations through a wireless network. An LBS server can use the location information to answer various types of continuous queries. Due to hardware limitations, location data reported by the moving objects are often uncertain. In this paper, we study efficient methods for the execution of Continuous Possible Nearest Neighbor Query (CPoNNQ) that accesses imprecise location data. A CPoNNQ is a standing query (which is active during a period of time) such that, at any time point, all moving objects that have non-zero probabilities of being the nearest neighbor of a given query point are reported. To handle the continuous nature of a CPoNNQ, a simple solution is to require moving objects to continuously report their locations to the LBS server, which evaluates the query at every time step. To save communication bandwidth and mobile devices' batteries, we develop two filter-based protocols for CPoNNQ evaluation. Our protocols install "filter bounds" on moving objects, which suppress unnecessary location reporting and communication between the server and the moving objects. Through extensive experiments, we show that our protocols can effectively reduce communication costs while maintaining a high query quality.
Intent-aware temporal query modeling for keyword suggestion
Authors: Fredrik Johansson, Tobias Färdig, Vinay Jethava, Svetoslav Marinov
Venue: CIKM
Year: 2012

This paper presents a data-driven approach for capturing the temporal variations in user search behaviour by modeling the dynamic query relationships using query-log data. The dependence between different queries (in terms of the query words and latent user intent) is represented using hypergraphs which allows us to explore more complex relationships compared to graph-based approaches. This time-varying dependence is modeled using the framework of probabilistic graphical models. The inferred interactions are used for query keyword suggestion - a key task in web information retrieval. Preliminary experiments using query logs collected from internal search engine of a large health care organization yield promising results. In particular, our model is able to capture temporal variations between queries relationships that reflect known trends in disease occurrence. Further, hypergraph-based modeling captures relationships significantly better compared to graph-based approaches.
Dual word and document seed selection for semi-supervised sentiment classification
Authors: Shengfeng Ju, Shoushan Li, Yan Su, Guodong Zhou, Yu Hong, Xiaojun Li
Venue: CIKM
Year: 2012

Semi-supervised sentiment classification aims to train a classifier with a small number of labeled data (called seed data) and a large amount of unlabeled data. a big advantage of this approach is its saving of annotation effort by using the unlabeled data which is usually freely available. In this paper, we propose an approach to further minimize the annotation effort of semi-supervised sentiment classification by actively selecting the seed data. Specifically, a novel selection strategy is proposed to simultaneously select good words and documents for manual annotation by considering both of their annotation costs and informativeness. Experimental results demonstrate the effectiveness of our approach.
MOUNA: mining opinions to unveil neglected arguments
Authors: Mouna Kacimi, Johann Gamper
Venue: CIKM
Year: 2012

A query topic can be subjective involving a variety of opinions, judgments, arguments, and many other debatable aspects. Typically, search engines process queries independently from the nature of their topics using a relevance-based retrieval strategy. Hence, search results about subjective topics are often biased towards a specific view point or version. In this demo, we shall present MOUNA, a novel approach for opinion diversification. Given a query on a subjective topic, MOUNA ranks search results based on three scores: (1) relevance of documents, (2) semantic diversity to avoid redundancy and capture the different arguments used to discuss the query topic, and (3) sentiment diversity to cover a balanced set of documents having positive, negative, and neutral sentiments about the query topic. Moreover, MOUNA enhances the representation of search results with a summary of the different arguments and sentiments related to the query topic. Thus, the user can navigate through the results and explore the links between them. We provide an example scenario in this demonstration to illustrate the inadequacy of relevance-based techniques for searching subjective topics and highlight the innovative aspects of MOUNA. A video showing the demo can be found in http://www.youtube.com/user/mounakacimi/videos .
Content-based crowd retrieval on the real-time web
Authors: Krishna Yeswanth Kamath, James Caverlee
Venue: CIKM
Year: 2012

In this paper, we propose and evaluate a novel content-driven crowd discovery algorithm that can efficiently identify newly-formed communities of users from the real-time web. Short-lived crowds reflect the real-time interests of their constituents and provide a foundation for user-focused web monitoring. Three of the salient features of the algorithm are its: (i) prefix-tree based locality-sensitive hashing approach for discovering crowds from high-volume rapidly-evolving social media; (ii) efficient user profile updating for incorporating new user activities and fading older ones; and (iii) key dimension identification, so that crowd detection can be focused on the most active portions of the real-time web. Through extensive experimental study, we find significantly more efficient crowd discovery as compared to both a k-means clustering-based approach and a MapReduce-based implementation, while maintaining high-quality crowds as compared to an offline approach. Additionally, we find that expert crowds tend to be "stickier" and last longer in comparison to crowds of typical users.
Spatial influence vs. community influence: modeling the global spread of social media
Authors: Krishna Yeswanth Kamath, James Caverlee, Zhiyuan Cheng, Daniel Z. Sui
Venue: CIKM
Year: 2012

In this paper we seek to understand and model the global spread of social media. How does social media spread from location to location across the globe? Can we model this spread and predict where social media will be popular in the future? Toward answering these questions, we develop a probabilistic model that synthesizes two conflicting hypotheses about the nature of online information spread: (i) the spatial influence model, which asserts that social media spreads to locations that are close by; and (ii) the community affinity influence model, which asserts that social media spreads between locations that are culturally connected, even if they are distant. Based on the geospatial footprint of 755 million geo-tagged hashtags spread through Twitter, we evaluate these models at predicting locations that will adopt hashtags in the future. We find that distance is the single most important explanation of future hashtag adoption since hashtags are fundamentally local. We also find that community affinities (like culture, language, and common interests) enhance the quality of purely spatial models, indicating the necessity of incorporating non-spatial features into models of global social media spread.
Predicting primary categories of business listings for local search
Authors: Changsung Kang, Jeehaeng Lee, Yi Chang
Venue: CIKM
Year: 2012

We consider the problem of identifying primary categories of a business listing among the categories provided by the owner of the business. The category information submitted by business owners cannot be trusted with absolute certainty since they may purposefully add some secondary or irrelevant categories to increase recall in local search results, which makes category search very challenging for local search engines. Thus, identifying primary categories of a business is a crucial problem in local search. This problem can be cast as a multi-label classification problem with a large number of categories. However, the large scale of the problem makes it infeasible to use conventional supervised-learning-based text categorization approaches.
Climbing the app wall: enabling mobile app discovery through context-aware recommendations
Authors: Alexandros Karatzoglou, Linas Baltrunas, Karen Church, Matthias Böhmer
Venue: CIKM
Year: 2012

The explosive growth of the mobile application (app) market has made it difficult for users to find the most interesting and relevant apps from the hundreds of thousands that exist today. Context is key in the mobile space and so too are proactive services that ease user input and facilitate effective interaction. We believe that to enable truly novel mobile app recommendation and discovery, we need to support real context-aware recommendation that utilizes the diverse range of implicit mobile data available in a fast and scalable manner. In this paper we introduce the Djinn model, a novel context-aware collaborative filtering algorithm for implicit feedback data that is based on tensor factorization. We evaluate our approach using a dataset from an Android mobile app recommendation service called appazaar. Our results show that our approach compares favorably with state-of-the-art collaborative filtering methods.