OpenRaiser/Intern-Atlas huggingface dataset:
Accelerating probabilistic frequent itemset mining: a model-based approach
Authors: Liang Wang, Reynold Cheng, Sau Dan Lee, David Wai-Lok Cheung
Venue: CIKM
Year: 2010

Data uncertainty is inherent in emerging applications such as location-based services, sensor monitoring systems, and data integration. To handle a large amount of imprecise information, uncertain databases have been recently developed. In this paper, we study how to efficiently discover frequent itemsets from large uncertain databases, interpreted under the Possible World Semantics. This is technically challenging, since an uncertain database induces an exponential number of possible worlds. To tackle this problem, we propose a novel method to capture the itemset mining process as a Poisson binomial distribution. This model-based approach extracts frequent itemsets with a high degree of accuracy, and supports large databases. We apply our techniques to improve the performance of the algorithms for: (1) finding itemsets whose frequentness probabilities are larger than some threshold; and (2) mining itemsets with the k highest frequentness probabilities. Our approaches support both tuple and attribute uncertainty models, which are commonly used to represent uncertain databases. Extensive evaluation on real and synthetic datasets shows that our methods are highly accurate. Moreover, they are orders of magnitudes faster than previous approaches.
Explore click models for search ranking
Authors: Dong Wang, Weizhu Chen, Gang Wang, Yuchen Zhang, Botao Amber Hu
Venue: CIKM
Year: 2010

Recent advances in click model have positioned it as an effective approach to estimate document relevance based on user behavior in web search. Yet, few works have been conducted to explore the use of click model to help web search ranking. In this paper, we focus on learning a ranking function by taking the results from a click model into account. Thus, besides the editorial relevance data arising from the explicit manually labeled search result by experts, we also have the estimated relevance data that is automatically inferred from click models based on user search behavior. We carry out extensive experiments on large-scale commercial datasets and demonstrate the effectiveness of the proposed methods.
Exploring online social activities for adaptive search personalization
Authors: Qihua Wang, Hongxia Jin
Venue: CIKM
Year: 2010

The web has largely become a very social environment and will continue to become even more so. People are not only enjoying their social visibility on the Web but also increasingly participating in various social activities delivered through the Web. In this paper, we propose to explore a user's public social activities, such as blogging and social bookmarking, to personalize Internet services. We believe that public social data provides a more acceptable way to derive user interests than more private data such as search histories and desktop data. We propose a framework that learns about users' preferences from their activities on a variety of online social systems. As an example, we illustrate how to apply the user interests derived by our system to personalize search results. Furthermore, our system is adaptive; it observes users' choices on search results and automatically adjusts the weights of different social systems during the information integration process, so as to refine its interest profile for each user. We have implemented our approach and performed experiments on real-world data collected from three large-scale online social systems. Over two hundred users from worldwide who are active on the three social systems have been tested. Our experimental results demonstrate the effectiveness of our personalized search approach. Our results also show that integrating information from multiple social systems usually leads to better personalized results than relying on the information from a single social system, and our adaptive approach further improves the performance of the personalization solution.
Collaboration analytics: mining work patterns from collaboration activities
Authors: Qihua Wang, Hongxia Jin, Yan Liu
Venue: CIKM
Year: 2010

People are increasingly using more and more social softwares, generating flooding communications. User analytics may be performed to mine a person's activities on different social systems and extract patterns, be it interest patterns, social patterns, or work patterns. Such patterns may benefit both the individuals and the organizations the users associated with, as the information is valuable in numerous tasks, including recommendation, evaluation, management, and so on. In this article, we present an actionable solution of user analytics, namely collaboration analytics, by focusing on mining a person's work patterns from her collaboration activities. Our solution effectively makes use of a user's heterogeneous data collected from various collaboration tools to derive an integrated description of the user's collaborative work. A number of ``work areas'', each of which contains its work topics and people involved, are generated for every user. The challenges we face include the clustering of items with short texts and prioritizing/weighting data items based on importance/relevance. Our solutions to those issues will be described in this article. In particular, we mine users' background information from various types of data and use such information to enrich the semantics of the short texts contained in the activity instances on collaboration tools before clustering those instances into work areas. Finally, we have developed a prototype of our collaboration analytics solution and evaluated it with real-world data and people.
Document update summarization using incremental hierarchical clustering
Authors: Dingding Wang, Tao Li
Venue: CIKM
Year: 2010

Document summarization has become a hot topic in recent years. However, most of existing summarization methods work on a batch of documents and do not consider that documents may arrive in a sequence and the corresponding summaries need to be updated in real time. In this paper, we propose a new summarization method based on an incremental hierarchical clustering framework to update summaries as soon as a new document arrives. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed method.
Ranking under temporal constraints
Authors: Lidan Wang, Donald Metzler, Jimmy Lin
Venue: CIKM
Year: 2010

This paper introduces the notion of temporally constrained ranked retrieval, which, given a query and a time constraint, produces the best possible ranked list within the specified time limit. Naturally, more time should translate into better results, but the ranking algorithm should always produce some results. This property is desirable from a number of perspectives: to cope with diverse users and information needs, as well as to better manage system load and variance in query execution times. We propose two temporally constrained ranking algorithms based on a class of probabilistic prediction models that can naturally incorporate efficiency constraints: one that makes independent feature selection decisions, and the other that makes joint feature selection decisions. Experiments on three different test collections show that both ranking algorithms are able to satisfy imposed time constraints, although the joint model outperforms the independent model in being able to deliver more effective results, especially under tight time constraints, due to its ability to capture feature dependencies.
Selecting keywords for content based recommendation
Authors: Christian Wartena, Wout Slakhorst, Martin Wibbels
Venue: CIKM
Year: 2010

The continued growth of online content makes personalized recommendation an increasingly important tool for media consumption. While collaborative filtering techniques have shown to be very successful in stable collections, content based approaches are necessary for recommending new items. Content based recommendation uses the similarity between new items and consumed items to predict whether a new item is interesting for the user. The similarity is computed by comparing the content or the meta-data of the items. In this paper we consider recommendation of TV-broadcasts for which meta-data and synopses are available. We thereby concentrate on the new item problem. We investigate the value of different types of meta-data provided by the broadcaster or extracted from synopsis. We show that extracted keywords are better suited for recommendation than manually assigned keywords. Furthermore we show that the number of keywords used is of great importance. Using a rather small number of keywords to present an item yields the best results for recommendation.
Assessor error in stratified evaluation
Authors: William Webber, Douglas W. Oard, Falk Scholer, Bruce Hedin
Venue: CIKM
Year: 2010

Several important information retrieval tasks, including those in medicine, law, and patent review, have an authoritative standard of relevance, and are concerned about retrieval completeness. During the evaluation of retrieval effectiveness in these domains, assessors make errors in applying the standard of relevance, and the impact of these errors, particularly on estimates of recall, is of crucial concern. Using data from the interactive task of the TREC Legal Track, this paper investigates how reliably the yield of relevant documents can be estimated from sampled assessments in the presence of assessor error, particularly where sampling is stratified based upon the results of participating retrieval systems. We show that assessor error is in general a greater source of inaccuracy than sampling error. A process of appeal and adjudication, such as used in the interactive task, is found to be effective at locating many assessment errors; but the process is expensive if complete, and biased if incomplete. An unbiased double-sampling method for resolving assessment error is proposed, and shown on representative data to be more efficient and accurate than appeal-based adjudication.
Demographic information flows
Authors: Ingmar Weber, Alejandro Jaimes
Venue: CIKM
Year: 2010

In advertising and content relevancy prediction it is important to understand whether, over time, information that reaches one demographic group spreads to others. In this paper we analyze the query log of a large U.S. web search engine to determine whether the same queries are performed by different demographic groups at different times, particularly when there are query bursts. We obtain aggregate demographic features from user-provided registration information (gender, birth year, ZIP code), U.S. census data, and election results. Given certain queries, we examine trends (from high to low and vice versa) and changes in the statistical spread of the demographic features of users that issue the queries over time periods that include query bursts. Our analysis shows that for certain types of queries (movies and news) distinct demographic groups perform searches at different times, suggesting that information related to such queries flows between them. Queries of movie titles, for instance, tend to be issued first by young and then by older users, where a sudden jump in age occurs upon the movie's release. To the best of our knowledge, this is the first time this problem has been studied using search query logs.
Generating advertising keywords from video content
Authors: Michael J. Welch, Junghoo Cho, Walter Chang
Venue: CIKM
Year: 2010

With the proliferation of online distribution methods for videos, content owners require easier and more effective methods for monetization through advertising. Matching advertisements with related content has a significant impact on the effectiveness of the ads, but current methods for selecting relevant advertising keywords for videos are limited by reliance on manually supplied metadata. In this paper we study the feasibility of using text available from video content to obtain high quality keywords suitable for matching advertisements. In particular, we tap into three sources of text for ad keyword generation: production scripts, closed captioning tracks, and speech-to-text transcripts. We address several challenges associated with using such data. To overcome the high error rates prevalent in automatic speech recognition and the lack of an explicit structure to provide hints about which keywords are most relevant, we use statistical and generative methods to identify dominant terms in the source text. To overcome the sparsity of the data and resulting vocabulary mismatches between source text and the advertiser's chosen keywords, these terms are then expanded into a set of related keywords using related term mining methods. Our evaluations present a comprehensive analysis of the relative performance for these methods across a range of videos, including professionally produced films and popular videos from YouTube.
Mapping web pages to database records via link paths
Authors: Tim Weninger, Fabio Fumarola, Jiawei Han, Donato Malerba
Venue: CIKM
Year: 2010

In this paper we propose a new knowledge management task which aims to map Web pages to their corresponding records in a structured database. For example, the DBLP database contains records for many computer scientists, and most of these persons have public Web pages; if we can map the database record with the appropriate Web page then the new information could be used to further describe the person's database record. To accomplish this goal we employ link paths which contain anchor texts from multiple paths through the Web ending at the Web page in question. We hypothesize that the information from these link paths can be used to generate an accurate Web page to database record mapping. Experiments on two large, real world data sets, DBLP and IMDB for the structured data and computer science faculty members' Web pages and official movie homepages for the Web page data, show that our method does provide an accurate mapping. Finally, we conclude by issuing a call for further research on this promising new task.
Components for information extraction: ontology-based information extractors and generic platforms
Authors: Daya C. Wimalasuriya, Dejing Dou
Venue: CIKM
Year: 2010

Information Extraction (IE) has existed as a field for several decades and has produced some impressive systems in the recent past. Despite its success, widespread usage and commercialization remain elusive goals for this field. We identify the lack of effective mechanisms for reuse as one major reason behind this situation. Here, we mean not only the reuse of the same IE technique in different situations but also the reuse of information related to the application of IE techniques (e.g., features used for classification).
Pattern discovery for large mixed-mode database
Authors: Andrew K. C. Wong, Bin Wu, Gene P. K. Wu, Keith C. C. Chan
Venue: CIKM
Year: 2010

In business and industry today, large databases with mixed data types (continuous and categorical) are very common. There are great needs to discover patterns from them for knowledge interpretation and understanding. In the past, for classification, this problem is solved as a discrete data problem by first discretizing the continuous data based on the class-attribute interdependence relationship. However, so far no proper solution exists when class information is unavailable. Hence, important pattern post-processing tasks such as pattern clustering and summarization cannot be applied to mixed-mode data. This paper presents a new method for solving the problem. It is based on two essential concepts. (1) Though class information is absent, yet for a correlated dataset, the attribute with the strongest interdependence with others in the group can be used to drive the discretization of the continuous data. (2) For a large database, correlated attribute groups must first be obtained by attribute clustering before (1) can be applied. Based on (1) and (2), pattern discovery methods are developed for mixed-mode data. Extensive experiments using synthetic and real world data were conducted to validate the usefulness and effectiveness of the proposed method.
Searching consumer image collections using web-based concept expansion
Authors: Mark D. Wood, Alexander C. Loui, Stacie Hibino
Venue: CIKM
Year: 2010

As consumers accumulate more and more personal imagery, searching for specific images has become increasingly difficult. Consumers typically provide little or no annotations, and automated classifiers and concept tagging tools are limited in their scope and vocabulary. This work addresses this sparsity of semantic information by leveraging domain-specific information provided by online photo-sharing communities. Such information enables improved search by allowing user-provided search terms to be expanded into a set of semantically related concepts, using relevant semantic relationships provided by millions of users. Our system first extracts metadata using a modest number of image and event-based semantic classifiers, as well as any meaningful file or folder names. When users pose text-based queries, our system retrieves images from their personal image collections by leveraging Flickr's tag dataset for concept expansion. This approach enables users to search their collections without having to manually annotate their pictures. We compare the retrieval performance of using a Flickr-based concept expander with the performance obtained without concept expansion and with using a WordNet-based concept expander. The results demonstrate that common sense knowledge gleaned from online photo sharing communities can enable meaningful image search on consumer image collections, searches that would be impossible using only the available image metadata.
Group ranking with application to image retrieval
Authors: Ou Wu, Weiming Hu, Bing Li
Venue: CIKM
Year: 2010

Many existing ranking-related information processing applications can be summarized into one theoretical problem called group ranking (GR). A simple average-ranking approach is usually applied to GR. Although the approach seems reasonable, no theoretical analysis about its intrinsic mechanism has been presented, increasing the difficulty of evaluating the ranking results. This study provides a formal analysis for GR. We first construct an objective function for the GR problem, and discover that each GR problem can be transformed into a rank aggregation problem whose objective function is proved to be equal to the objective function of GR. As a consequence, the average-ranking approach can be explained by two well-known rank aggregation techniques. We incorporate two other effective rank aggregation methods into the GR problem and obtain two new GR algorithms. We apply the GR algorithms into image retrieval to diversify the image search results returned by search engines. Experimental results show the effectiveness of the proposed GR algorithms.
Elusive vandalism detection in wikipedia: a text stability-based approach
Authors: Qinyi Wu, Danesh Irani, Calton Pu, Lakshmish Ramaswamy
Venue: CIKM
Year: 2010

The open collaborative nature of wikis encourages participation of all users, but at the same time exposes their content to vandalism. The current vandalism-detection techniques, while effective against relatively obvious vandalism edits, prove to be inadequate in detecting increasingly prevalent sophisticated (or elusive) vandal edits. We identify a number of vandal edits that can take hours, even days, to correct and propose a text stability-based approach for detecting them. Our approach is focused on the likelihood of a certain part of an article being modified by a regular edit. In addition to text-stability, our machine learning-based technique also takes into account edit patterns. We evaluate the performance of our approach on a corpus comprising of 15000 manually labeled edits from the Wikipedia Vandalism PAN corpus. The experimental results show that text-stability is able to improve the performance of the selected machine-learning algorithms significantly.
K-farthest-neighbors-based concept boundary determination for support vector data description
Authors: Yanshan Xiao, Bo Liu, Longbing Cao
Venue: CIKM
Year: 2010

Support vector data description (SVDD) is very useful for one-class classification. However, it incurs high time complexity in handling large scale data. In this paper, we propose a novel and efficient method, named K-Farthest-Neighbors-based Concept Boundary Detection (KFN-CBD for short), to improve the SVDD learning efficiency on large datasets. This work is motivated by the observation that SVDD classifier is determined by support vectors (SVs), and removing the non-support vectors (non-SVs) will not change the classifier but will reduce computational costs. Our approach consists of two steps. In the first step, we propose the K-farthest-neighbors method to identify the samples around the hyper-sphere surface, which are more likely to be SVs. At the same time, a new tree search strategy of M-tree is presented to speed up the K-farthest neighbor query. In the second step, the non-SVs are eliminated from the training set, and only the identified boundary samples are used to train the SVDD classifier. By removing the non-SVs, the training time of SVDD can be substantially reduced.Extensive experiments have shown that KFN-CBD achieves around 6 times speedup compared to the standard SVDD, and obtains the comparable classification quality as the entire dataset used.
Modeling reformulation using passage analysis
Authors: Xiaobing Xue, W. Bruce Croft, David A. Smith
Venue: CIKM
Year: 2010

Query reformulation modifies the original query with the aim of better matching the vocabulary of the relevant documents, and consequently improving ranking effectiveness. Previous techniques typically generate words and phrases related to the original query, but do not consider how these words and phrases would fit together in new queries. In this paper, we focus on an implementation of an approach that models reformulation as a distribution of queries, where each query is a variation of the original query. This approach considers a query as a basic unit and can capture important dependencies between words and phrases in the query. The implementation discussed here is based on passage analysis of the target corpus. Experiments on the TREC collection show that the proposed model for query reformulation significantly outperforms state-of-the-art methods.
Facetedpedia: enabling query-dependent faceted search for wikipedia
Authors: Ning Yan, Chengkai Li, Senjuti Basu Roy, Rakesh Ramegowda, Gautam Das
Venue: CIKM
Year: 2010

Facetedpedia is a faceted search system that dynamically discovers query-dependent faceted interfaces for Wikipedia search result articles. In this paper, we give an overview of Facetedpedia, present the system architecture and implementation techniques, and elaborate on a demonstration scenario.
MI-WDIS: web data integration system for market intelligence
Authors: Zhongmin Yan, Qingzhong Li, Shidong Zhang, Zhaohui Peng, Yongquan Dong, Yanhui Ding, Yongxin Zhang, Xiuxing Xu
Venue: CIKM
Year: 2010

As an important supporting technology of Market Intelligence (MI), Web data integration is facing new challenges, such as the integrity of data acquisition, the quality of data extraction and data consolidation. To solve such problems, we propose an MI-oriented web data integration system (MI-WDIS), which achieves excellent performances in integrating Surface Web and Deep Web data with much less manual work. Based on MI-WDIS, we have developed a platform for intelligent analysis of job data. The platform collects tens of thousands of job data daily and provides personalized services for job seekers through diversified channels. Besides, it provides other advanced services, including intelligence analysis, automatic monitoring and alerting, for various organizations, such as enterprises, training institutions and recruitment agencies.