OpenRaiser/Intern-Atlas huggingface dataset:
Exploring path query results through relevance feedback
Authors: Huiping Cao, Yan Qi, K. Selçuk Candan, Maria Luisa Sapino
Venue: CIKM
Year: 2009

Feedback driven data exploration schemes have been implemented for non-structured data (such as text) and document-centric XML collections where formulating precise queries is often impossible. In this paper, we study the problem of enabling exploratory access, through ranking, to data-centric XML. Given a path query and a set of results identified by the system to this query over the data, we consider feedback which captures the user's preference for some features over the others. The feedback can be "positive" or "negative". To deal with feedback, we develop a probabilistic feature significance measure and describe how to use this for ranking results in the presence of dependencies between the path features. We bring together these techniques in AXP, a system for adaptive and exploratory path retrieval. The experimental results show the effectiveness of the proposed techniques.
PQC: personalized query classification
Authors: Bin Cao, Jian-Tao Sun, Evan Wei Xiang, Derek Hao Hu, Qiang Yang, Zheng Chen
Venue: CIKM
Year: 2009

Query classification (QC) is a task that aims to classify Web queries into topical categories. Since queries are usually short in length and ambiguous, the same query may need to be classified to different categories according to different people's perspectives. In this paper, we propose the Personalized Query Classification (PQC) task and develop an algorithm based on user preference learning as a solution. Users' preferences that are hidden in clickthrough logs are quite helpful for search engines to improve their understandings of users' queries. We propose to connect query classification with users' preference learning from clickthrough logs for PQC. To tackle the sparseness problem in clickthrough logs, we propose a collaborative ranking model to leverage similar users' information. Experiments on a real world clickthrough log data show that our proposed PQC algorithm can gain significant improvement compared with general QC as well as natural baselines. Our method can be applied to a wide range of applications including personalized search and online advertising.
Who tags the tags?: a framework for bookmark weighting
Authors: David Carmel, Haggai Roitman, Elad Yom-Tov
Venue: CIKM
Year: 2009

In this work we propose a novel framework for bookmark weighting which allows us to estimate the effectiveness of each of the bookmarks individually. We show that by weighting bookmarks according to their estimated quality we can significantly improve search effectiveness. Using empirical evaluation on real data gathered from two large bookmarking systems, we demonstrate the effectiveness of the new framework for search enhancement.
Personalized social search based on the user's social network
Authors: David Carmel, Naama Zwerdling, Ido Guy, Shila Ofek-Koifman, Nadav Har&apos, El, Inbal Ronen, Erel Uziel, Sivan Yogev, Sergey Chernov
Venue: CIKM
Year: 2009

This work investigates personalized social search based on the user’s social relations – search results are re-ranked according to their relations with individuals in the user’s social network. We study the effectiveness of several social network types for personalization: (1) Familiarity-based network of people related to the user through explicit familiarity connection; (2) Similarity-based network of people “similar ” to the user as reflected by their social activity; (3) Overall network that provides both relationship types. For comparison we also experiment with Topic-based personalization that is based on the user’s related terms, aggregated from several social applications. We evaluate the contribution of the different personalization strategies by an off-line study and by a user survey within our organization. In the off-line study we apply bookmark-based evaluation, suggested recently, that exploits data gathered from a social bookmarking system to evaluate personalized retrieval. In the on-line study we analyze the feedback of 240 employees exposed to the alternative personalization approaches. Our main results show that both in the off-line study and in the user survey social network based personalization significantly outperforms non-personalized social search. Additionally, as reflected by the user survey, all three SN-based strategies significantly outperform the Topic-based strategy.
Exploring relevance for clicks
Authors: Rongwei Cen, Yiqun Liu, Min Zhang, Bo Zhou, Liyun Ru, Shaoping Ma
Venue: CIKM
Year: 2009

Mining feedback information from user click-through data is an important issue for modern Web retrieval systems in terms of architecture analysis, performance evaluation and algorithm optimization. For commercial search engines, user click-through data contains useful information as well as large amount of inevitable noises. This paper proposes an approach to recognize reliable and meaningful user clicks (referred to as Relevant Clicks, RCs) in click-through data. By modeling user click-through behavior on search result lists, we propose several features to separate RCs from click noises. A learning algorithm is presented to estimate the quality of user clicks. Experimental results on large scale dataset show that: 1) our model effectively identifies RCs in noisy click-through data; 2) Different from previous click-through analysis efforts, our approach works well for both hot queries and long-tail queries.
Agglomerating local patterns hierarchically with ALPHA
Authors: Loïc Cerf, Pierre-Nicolas Mougel, Jean-François Boulicaut
Venue: CIKM
Year: 2009

To increase the relevancy of local patterns discovered from noisy relations, it makes sense to formalize error-tolerance. Our starting point is to address the limitations of state-of-the-art methods for this purpose. Some extractors perform an exhaustive search w.r.t. a declarative specification of error-tolerance. Nevertheless, their computational complexity prevents the discovery of large relevant patterns. Alpha is a 3-step method that (1) computes complete collections of closed patterns, possibly error-tolerant ones, from arbitrary n-ary relations, (2) enlarges them by hierarchical agglomeration, and (3) selects the relevant agglomerated patterns.
Learning from past queries for resource selection
Authors: Suleyman Cetintas, Luo Si, Hao Yuan
Venue: CIKM
Year: 2009

Federated text search provides a unified search interface for multiple search engines of distributed text information sources. Resource selection is an important component for federated text search, which selects a small number of information sources that contain the largest number of relevant documents for a user query. Most prior research of resource selection focused on selecting information sources by analyzing static information of available information sources that is sampled in the offline manner. On the other hand, most prior research ignored a large amount of valuable information like the results from past queries. This paper proposes a new resource selection technique (which is called qSim) that utilizes the search results of past queries for estimating the utilities of available information sources for a specific user query. Experiment results demonstrate the effectiveness of the new resource selection algorithm.
Fast centrality approximation in modular networks
Authors: Shu Yan Chan, Ian X. Y. Leung, Pietro Liò
Venue: CIKM
Year: 2009

Measuring the centrality of nodes in real-world networks has remained an important task in the technological, social, and biological network paradigms carrying implications on their analysis and applications. Exact inference of centrality values is infeasible in large networks due to the need to solve the all-pairs shortest path problem. We introduce a framework to approximate node centralities in real-world networks that are known to exhibit modularity, i.e., the presence of dense subgraphs or communities, which are themselves sparsely connected. We also propose a novel centrality measure known as Community Inbetweenness that ranks nodes based solely on community information. In a modular network of size n with √n(1/2) evenly sized communities and m edges, our framework requires linear time O(m) and O(√nm) time for the approximation of closeness and betweenness respectively. Utilizing a recently proposed linear time method in community detection, our approximation techniques are faster than traditional sampling algorithms, applicable in real-time distributed environments, and offer highly comparable results.
Context-sensitive document ranking
Authors: Lijun Chang, Jeffrey Xu Yu, Lu Qin
Venue: CIKM
Year: 2009

Ranking is a main research issue in IR-styled keyword search over a set of documents. In this paper, we study a new keyword search problem, called context-sensitive document ranking, which is to rank documents with an additional context that provides additional information about the application domain where the documents are to be searched and ranked. The work is motivated by the fact that additional information associated with the documents can possibly assist users to find more relevant documents when they are unable to find the needed documents from the documents alone. In this paper, a context is a multi-attribute graph, which can represent any information maintained in a relational database. The context-sensitive ranking is related to several research issues, how to score documents, how to evaluate the additional information obtained in the context that may contribute the document ranking, how to rank the documents by combining the scores/costs from the documents and the context. More importantly, the relationships between documents and the information stored in a relational database may be uncertain, because they are from different data sources and the relationships are determined systematically using similarity match which causes uncertainty. In this paper, we concentrate ourselves on these research issues, and provide our solution on how to rank the documents in a context where there exist uncertainty between the documents and the context. We confirm the effectiveness of our approaches by conducting extensive experimental studies using real datasets.
On domain similarity and effectiveness of adapting-to-rank
Authors: Keke Chen, Jing Bai, Srihari Reddy, Belle L. Tseng
Venue: CIKM
Year: 2009

Adapting to rank address the problem of insufficient domain-specific labeled training data in learning to rank. However, the initial study shows that adaptation is not always effective. In this paper, we investigate the relationship between the domain similarity and the effectiveness of domain adaptation with the help of two domain similarity measure: relevance correlation and sample distribution correlation.
Efficient information retrieval in mobile peer-to-peer networks
Authors: Lijiang Chen, Bin Cui, Heng Tao Shen, Wei Lu, Xiaofang Zhou
Venue: CIKM
Year: 2009

Mobile devices have become indispensable in daily life, and hence how to take advantage of these portable and powerful facilities to share resources and information begins to emerge as an interesting problem. In this paper, we investigate the problem of information retrieval in a mobile peer-to-peer network. The prevailing approach to information retrieval is to apply flooding methods because of its quick response and easy maintenance. Obviously, this kind of approach wastes a huge amount of communication bandwidth which greatly affects the availability of the network, and the battery power which significantly shortens the serving time of mobile devices in the network. To tackle this problem, we propose a novel approach by mimicking different human behaviors of social networks, which takes advantages of Intelligence Accuracy (IA) mechanism that evaluates the distance from a node to certain resources in the network. Extensive experimental results show the efficiency and effectiveness of our approach as well as its scalability in a volatile environment.
iMecho: an associative memory based desktop search system
Authors: Jidong Chen, Hang Guo, Wentao Wu, Wei Wang
Venue: CIKM
Year: 2009

Traditional desktop search engines only support keyword based search that needs exact keyword matching to find resources. However, users generally have a vague picture of what is stored but forget the exact location and keywords of the resource. According to observations of human associative memory, people tend to remember things from some memory fragments in their brains and these memory fragments are connected by memory cues of user activity context. We developed iMecho (My Memory Echo), an associative memory based desktop search system, which exploits such associations and contexts to enhance traditional desktop search. Desktop resources are connected with semantic links mined from explicit and implicit user activities according to specific access patterns. Using these semantic links, associations among memory fragments can be built or rebuilt in a user's brain during a search. Moreover, our personalized ranking scheme uses these links together with a user's personal preferences to rank results by both relevance and importance to the user. In addition, the system provides a faceted search feature and association graph navigation to help users refine and associate search results generated by full-text keyword search. Our experiments investigating precision and recall quality of iMecho prototype show that the association-based search system is superior to the traditional keyword search in personal search engines since it is closer to the way that human associative memory works.
Message family propagation for ising mean field based on iteration tree
Authors: Yarui Chen, Shizhong Liao
Venue: CIKM
Year: 2009

Ising mean field is a basic variational inference method for Ising model, which can provide an effective approximate solution for large-scale inference problem. The main idea is to transform a probabilistic inference problem into a functional extremum problem by variational calculus, and solve the functional extremum problem to obtain approximate marginal distributions. The process of solving the functional extremum is an important step and a computational core for variational inference. But the traditional full variational iteration methods make the variable information intercross with each other deeply. From the view of incomplete variational iterations, we propose a message family propagation method for Ising mean field to compute a marginal distribution family of object variable.
Instance- and bag-level manifold regularization for aggregate outputs classification
Authors: Shuo Chen, Bin Liu, Mingjie Qian, Changshui Zhang
Venue: CIKM
Year: 2009

Aggregate outputs learning differs from the classical supervised learning setting in that, training samples are packed into bags with only the aggregate outputs (labels for classification or real values for regression) known. This setting of the problem is associated with several kinds of application background. We focus on the aggregate outputs classification problem in this paper, and set up a manifold regularization framework to deal with it. The framework can be of both instance level and bag level for different testing goals. We propose four concrete algorithms based on our framework, each of which can cope with both binary and multi-class scenarios. The experimental results on several datasets suggest that our algorithms outperform the stateof-art technique.
Progressive skyline query evaluation and maintenance in wireless sensor networks
Authors: Baichen Chen, Weifa Liang, Jeffrey Xu Yu
Venue: CIKM
Year: 2009

Skyline query has been received much attention due to its wide application backgrounds for multi-preference and decision making. In this paper we consider skyline query evaluation and maintenance in wireless sensor networks. We devise an evaluation algorithm for finding skyline points progressively and a maintenance algorithm for skyline maintenance incrementally. We also conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms on various datasets. The experimental results show that the proposed algorithms significantly outperform existing algorithms in terms of network lifetime prolongation.
Practical lessons of data mining at Yahoo!
Authors: Ye Chen, Dmitry Pavlov, Pavel Berkhin, Aparna Seetharaman, Albert Meltzer
Venue: CIKM
Year: 2009

The usage of data in many commercial applications has been growing at an unprecedented pace in the last decade. While successful data mining efforts lead to major business advances, there were also numerous, less publicized efforts that for one or another reason failed. In this paper, we discuss practical lessons based on years of our data mining experiences at Yahoo! and offer insights into how to drive the data mining effort to success in a business environment.
Event detection from flickr data through wavelet-based spatial analysis
Authors: Ling Chen, Abhishek Roy
Venue: CIKM
Year: 2009

Detecting events from web resources has attracted increasing
A co-classification framework for detecting web spam and spammers in social media web sites
Authors: Feilong Chen, Pang-Ning Tan, Anil K. Jain
Venue: CIKM
Year: 2009

Social media are becoming increasingly popular and have attracted considerable attention from spammers. Using a sample of more than ninety thousand known spam Web sites, we found between 7% to 18% of their URLs are posted on two popular social media Web sites, digg.com and delicious.com. In this paper, we present a co-classification framework to detect Web spam and the spammers who are responsible for posting them on the social media Web sites. The rationale for our approach is that since both detection tasks are related, it would be advantageous to train them simultaneously to make use of the labeled examples in the Web spam and spammer training data. We have evaluated the effectiveness of our algorithm on the delicious.com data set. Our experimental results showed that the proposed co-classification algorithm significantly outperforms classifiers that learn each detection task independently.
Learning to rank with a novel kernel perceptron method
Authors: Xue-wen Chen, Haixun Wang, Xiaotong Lin
Venue: CIKM
Year: 2009

While conventional ranking algorithms, such as the PageRank, rely on the web structure to decide the relevancy of a web page, learning to rank seeks a function capable of ordering a set of instances using a supervised learning approach. Learning to rank has gained increasing popularity in information retrieval and machine learning communities. In this paper, we propose a novel nonlinear perceptron method for rank learning. The proposed method is an online algorithm and simple to implement. It introduces a kernel function to map the original feature space into a nonlinear space and employs a perceptron method to minimize the ranking error by avoiding converging to a solution near the decision boundary and alleviating the effect of outliers in the training dataset. Furthermore, unlike existing approaches such as RankSVM and RankBoost, the proposed method is scalable to large datasets for online learning. Experimental results on benchmark corpora show that our approach is more efficient and achieves higher or comparable accuracies in instance ranking than state of the art methods such as FRank, RankSVM and RankBoost.
Efficient processing of group-oriented connection queries in a large graph
Authors: James Cheng, Yiping Ke, Wilfred Ng
Venue: CIKM
Year: 2009

We study query processing in large graphs that are fundamental data model underpinning various social networks and Web structures. Given a set of query nodes, we aim to find the groups which the query nodes belong to, as well as the best connection among the groups. Such a query is useful to many applications but the query processing is extremely costly. We define a new notion of Correlation Group (CG), which is a set of nodes that are strongly correlated in a large graph G. We then extract the subgraph from G that gives the best connection for the nodes in a CG. To facilitate query processing, we develop an efficient index built upon the CGs. Our experiments show that the CGs are meaningful as groups and importantly, the meaningfulness of the query results are justifiable. We also demonstrate the high efficiency of CG computation, index construction and query processing.
Integrating web-based intelligence retrieval and decision-making from the twitter trends knowledge base
Authors: Marc Cheong, Vincent C. S. Lee
Venue: CIKM
Year: 2009

Twitter as a microblogging platform has vast potential to become a collective source of intelligence that can be used to obtain opinions, ideas, facts, and sentiments. This paper addresses the issue on collective intelligence retrieval with activated knowledge-base decision making. Our methodology differs from the existing literature in the sense that we are doing analysis on Twitter microblog messages as opposed to traditional blog analysis in the literature which deals with the conventional 'blogosphere'. Another key difference in our methodology is that we apply visualization techniques in conjunction with artificial intelligence-based data mining methods to classify messages dealing with the trend topic. Our methodology also analyzes demographics of the authors of such Twitter messages and attempt to map a Twitter trend into what's going on in the real world. Our findings reveal a pattern behind trends on Twitter, enabling us to see how it 'ticks' and evolves though visualization methods. Our findings also enable us to understand the underlying characteristics behind the 'trend setters', providing us a new perspective on the contributors of a trend.
Ensembles in adversarial classification for spam
Authors: Deepak Chinavle, Pranam Kolari, Tim Oates, Tim Finin
Venue: CIKM
Year: 2009

The standard method for combating spam, either in email or on the web, is to train a classifier on manually labeled instances. As the spammers change their tactics, the performance of such classifiers tends to decrease over time. Gathering and labeling more data to periodically retrain the classifier is expensive. We present a method based on an ensemble of classifiers that can detect when its performance might be degrading and retrain itself, all without manual intervention. Experiments with a real-world dataset from the blog domain show that our methods can significantly reduce the number of times classifiers are retrained when compared to a fixed retraining schedule, and they maintain classification accuracy even in the absence of manually labeled examples.
SPIDER: a system for scalable, parallel / distributed evaluation of large-scale RDF data
Authors: Hyunsik Choi, Jihoon Son, YongHyun Cho, Min Kyoung Sung, Yon Dohn Chung
Venue: CIKM
Year: 2009

RDF is a data model for representing labeled directed graphs, and it is used as an important building block of semantic web. Due to its flexibility and applicability, RDF has been used in applications, such as semantic web, bioinformatics, and social networks. In these applications, large-scale graph datasets are very common. However, existing techniques are not effectively managing them. In this paper, we present a scalable, efficient query processing system for RDF data, named SPIDER, based on the well-known parallel/distributed computing framework, Hadoop. SPIDER consists of two major modules (1) the graph data loader, (2) the graph query processor. The loader analyzes and dissects the RDF data and places parts of data over multiple servers. The query processor parses the user query and distributes sub queries to cluster nodes. Also, the results of sub queries from multiple servers are gathered (and refined if necessary) and delivered to the user. Both modules utilize the MapReduce framework of Hadoop. In addition, our system supports some features of SPARQL query language. This prototype will be foundation to develop real applications with large-scale RDF graph data.