OpenRaiser/Intern-Atlas huggingface dataset:
The gist of everything new: personalized top-k processing over web 2.0 streams
Authors: Parisa Haghani, Sebastian Michel, Karl Aberer
Venue: CIKM
Year: 2010

Web 2.0 portals have made content generation easier than ever with millions of users contributing news stories in form of posts in weblogs or short textual snippets as in Twitter. Efficient and effective filtering solutions are key to allow users stay tuned to this ever-growing ocean of information, releasing only relevant trickles of personal interest. In classical information filtering systems, user interests are formulated using standard IR techniques and data from all available information sources is filtered based on a predefined absolute quality-based threshold. In contrast to this restrictive approach which may still overwhelm the user with the returned stream of data, we envision a system which continuously keeps the user updated with only the top-k relevant new information. Freshness of data is guaranteed by considering it valid for a particular time interval, controlled by a sliding window. Considering relevance as relative to the existing pool of new information creates a highly dynamic setting. We present POL-filter which together with our maintenance module constitute an efficient solution to this kind of problem. We show by comprehensive performance evaluations using real world data, obtained from a weblog crawl, that our approach brings performance gains compared to state-of-the-art.
Topic-driven web search result organization by leveraging wikipedia semantic knowledge
Authors: Xianpei Han, Jun Zhao
Venue: CIKM
Year: 2010

Effective organization of web search results can greatly improve the utility of search engine and enhance the quality of search results. However, the organization of search results is difficult because the sub-topics of a query are usually not explicitly given. In this paper, we propose a novel topic-driven search result organization method, which can first detect the sub-topics of a query by finding the coherent Wikipedia concept groups from its search results; then organize these results using a topic-driven clustering algorithm; in the end we score and rank the topics using the support vector regression model. Empirical results show that our method can achieve competitive performance.
CiteData: a new multi-faceted dataset for evaluating personalized search performance
Authors: Abhay Harpale, Yiming Yang, Siddharth Gopal, Daqing He, Zhen Yue
Venue: CIKM
Year: 2010

Personalized search systems have evolved to utilize heterogeneous features including document hyperlinks, category labels in various taxonomies and social tags in addition to free-text of the documents. Consequently, classifiers, PageRank algorithms and Collaborative Filtering methods are often used as intermediate steps in such personalized retrieval systems. Thorough comparative evaluation of such complex systems has been difficult due to the lack of appropriate publicly available datasets that provide such diverse feature sets. To remedy the situation, we have created CiteData, a new dataset for benchmark evaluations of personalized search performance, that will be made publicly accessible. CiteData is a collection of academic articles extracted from CiteULike and CiteSeer repositories, with rich feature sets such as authors, author-affiliations, topic labels, social tags and citation information. We further supplement it with personalized queries and relevance judgments which were obtained from volunteer users. This paper starts with a discussion of the design criteria and characteristics of the CiteData dataset in comparison with current benchmark datasets, followed by a set of task-oriented empirical evaluations of popular algorithms in statistical classification, collaborative filtering and link analysis as intermediate steps for personalized search. Our results show significant performance improvement of personalized approaches, over that of unpersonalized approaches. We also observe that a meta personalized search engine that leverages information from multiple sources of features performs better than algorithms that use only one of the constituent source of features.
Ranking social bookmarks using topic models
Authors: Morgan Harvey, Ian Ruthven, Mark James Carman
Venue: CIKM
Year: 2010

Ranking of resources in social tagging systems is a difficult problem due to the inherent sparsity of the data and the vocabulary problems introduced by having a completely unrestricted lexicon. In this paper we propose to use hidden topic models as a principled way of reducing the dimensionality of this data to provide more accurate resource rankings with higher recall. We first describe Latent Dirichlet Allocation (LDA) and then show how it can be used to rank resources in a social bookmarking system. We test the LDA tagging model and compare it with 3 non-topic model baselines on a large data sample obtained from the Delicious social bookmarking site. Our evaluations show that our LDA-based method significantly outperforms all of the baselines.
A comparison of user and system query performance predictions
Authors: Claudia Hauff, Diane Kelly, Leif Azzopardi
Venue: CIKM
Year: 2010

Query performance prediction methods are usually applied to estimate the retrieval effectiveness of queries, where the evaluation is largely system sided. However, little work has been conducted to understand query performance prediction from the user's perspective. The question we consider is, whether the predictions of query performance that systems make are in line with the predictions that users make. To this aim, we compare the performance ratings users assign to queries with the performance scores estimated by a range of pre-retrieval and post-retrieval query performance predictors. Two studies are presented that explore the relationship between user ratings and system predictions on two levels: (i) the topic level, and, (ii) the query suggestions level. It is shown that when predicting the performance of query suggestions, user ratings were mostly uncorrelated with system predictions. At the topic level though, where a single query is judged for each information need, we observed moderate correlations between user ratings and a subset of system predictions. As query performance prediction methods are often based on intuitions of how users might rate queries, these findings suggest that such methods are not representative of how users actually rate query suggestions and topics. This motivates further research into understanding the rating process engaged by users, and developing models of query performance prediction in order to bridge the divide between systems and users.
Learning sentiment classification model from labeled features
Authors: Yulan He
Venue: CIKM
Year: 2010

We propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon. Preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than exiting weakly-supervised sentiment classification methods despite using no labeled documents.
Directly optimizing evaluation measures in learning to rank based on the clonal selection algorithm
Authors: Qiang He, Jun Ma, Shuaiqiang Wang
Venue: CIKM
Year: 2010

One fundamental issue of learning to rank is the choice of loss function to be optimized. Although the evaluation measures used in Information Retrieval (IR) are ideal ones, in many cases they can't be used directly because they do not satisfy the smooth property needed in conventional machine learning algorithms. In this paper a new method named RankCSA is proposed, which tries to use IR evaluation measure directly. It employs the clonal selection algorithm to learn an effective ranking function by combining various evidences in IR. Experimental results on the LETOR benchmarh datasets demonstrate that RankCSA outperforms the baseline methods in terms of [email protected], MAP and [email protected]
Active caching for similarity queries based on shared-neighbor information
Authors: Michael E. Houle, Vincent Oria, Umar Qasim
Venue: CIKM
Year: 2010

Novel applications such as recommender systems, uncertain databases, and multimedia databases are designed to process similarity queries that produce ranked lists of objects as their results. Similarity queries typically result in disk access latency and incur a substantial computational cost. In this paper, we propose an 'active caching' technique for similarity queries that is capable of synthesizing query results from cached information even when the required result list is not explicitly stored in the cache. Our solution, the Cache Estimated Significance (CES) model, is based on shared-neighbor similarity measures, which assess the strength of the relationship between two objects as a function of the number of other objects in the common intersection of their neighborhoods. The proposed method is general in that it does not require that the features be drawn from a metric space, nor does it require that the partial orders induced by the similarity measure be monotonic. Experimental results on real data sets show a substantial cache hit rate when compared with traditional caching approaches.
Query model refinement using word graphs
Authors: Yunping Huang, Le Sun, Jian-Yun Nie
Venue: CIKM
Year: 2010

Pseudo relevance feedback method is an effective method for query model refinement. Most existing pseudo relevance feedback methods only take into consideration the term distribution of the feedback documents, but omit the term's context information. This paper presents a graph-based method to improve query models, in which a word graph is constructed to encode terms and their co-occurrence dependencies within the feedback documents. Using a random walk, the weight of each term in the graph can be determined in a context-dependent manner, i.e. the weight of a term is strongly dependent on the weights of the connected context terms. Our experimental results on four TREC collections show that our proposed approach is more effective than the existing state-of-the-art approaches.
Identifying the challenges for optimizing the process to achieve reproducible results in e-science applications
Authors: Mohammad Rezwanul Huq, Andreas Wombacher, Peter M. G. Apers
Venue: CIKM
Year: 2010

One of the major requirements for e-science applications handling sensor data, is reproducibility of results. Several optimization and scalability problems exist where the reproducibility of results remains guaranteed. Firstly, various data streams need to be coordinated to optimize the accuracy and processing of the results. Secondly, because of the high volume of streaming data and a series of processing steps to be performed on that data, demand for disk space may grow unacceptably high. Lastly, reproducibility in a decentralized scenario may be difficult to achieve because of data replication. This paper introduces and addresses these challenges which arise for optimizing the process of achieving reproducibility of results.
Organizing query completions for web search
Authors: Alpa Jain, Gilad Mishne
Venue: CIKM
Year: 2010

All state-of-the-art web search engines implement an auto-completion mechanism - an assistive technology enabling users to effectively formulate their search queries by predicting the next characters or words that they are likely to type. Query completions (or suggestions) are typically mined from past user interactions with the search engine, e.g., from query logs, clickthrough patterns, or query reformulations; they are ranked by some measure of query popularity, e.g., query frequency or clickthrough rate. Current query suggestion tools largely assume that the set of suggestions provided to the users is homogeneous, corresponding to a single real-world interpretation of the query. In this paper, we hypothesize that, in some cases, users would benefit from an alternative presentation of the suggestions, one where suggestions are not only ordered by likelihood but also organized by high-level user intent. Rich search suggestion interaction frameworks that reduce the user effort in identifying the set of relevant suggestions open new and promising directions towards improving user experience. Along these lines, we propose clustering the set of suggestions presented to a search engine user, and assigning an appropriate label to each subset of suggestions to help users quickly identify useful ones. For this, we present a variety of unsupervised clustering techniques for search suggestions, based on the information available to a large-scale web search engine. We evaluate our novel search suggestion presentation techniques on a real-world dataset of query logs. Based on a set of user studies, we show that by extending the existing assistance layer to effectively group suggestions and label them - while accounting for the query popularity - we substantially increase the user's satisfaction.
Exploiting sequential relationships for familial classification
Authors: Lee S. Jensen, James G. Shanahan
Venue: CIKM
Year: 2010

The pervasive nature of the internet has caused a significant transformation in the field of genealogical research. This has impacted not only how research is conducted, but has also dramatically increased the number of people discovering their family history. Recent market research (Maritz Marketing 2000, Harris Interactive 2009) indicates that general interest in the United States has increased from 45% in 1996, to 60% in 2000, and 87% in 2009. Increased popularity has caused a dramatic need for improvements in algorithms related to extracting, accessing, and processing genealogical data for use in building family trees. This paper presents one approach to algorithmic improvement in the family history domain, where we infer the familial relationships of households found in human transcribed United States census data. By applying advances made in natural language processing, exploiting the sequential nature of the census, and using state of the art machine learning algorithms, we were able to decrease the error by 35% over a hand coded baseline system. The resulting system is immediately applicable to hundreds of millions of other genealogical records where families are represented, but the familial relationships are missing.
Visual cube and on-line analytical processing of images
Authors: Xin Jin, Jiawei Han, Liangliang Cao, Jiebo Luo, Bolin Ding, Cindy Xide Lin
Venue: CIKM
Year: 2010

On-Line Analytical Processing (OLAP) has shown great success in many industry applications, including sales, marketing, management, financial data analysis, etc. In this paper, we propose Visual Cube and multi-dimensional OLAP of image collections, such as web images indexed in search engines (e.g., Google and Bing), product images (e.g. Amazon) and photos shared on social networks (e.g., Facebook and Flickr). It provides online responses to user requests with summarized statistics of image information and handles rich semantics related to image visual features. A clustering structure measure is proposed to help users freely navigate and explore images. Efficient algorithms are developed to construct Visual Cube. In addition, we introduce the new issue of Cell Overlapping in data cube and present efficient solutions for Visual Cube computation and OLAP operations. Extensive experiments are conducted and the results show good performance of our algorithms.
Discovering, ranking and annotating cross-document relationships between concepts
Authors: Wei Jin, Xin Wu
Venue: CIKM
Year: 2010

This paper presents CDRMiner, a system for automatically discovering, ranking and annotating cross-document links between concepts. Specifically, we focus on detecting hidden associations between two concepts and further generating annotations for each discovered hypothesis. We interpret such a relationship query as finding the most meaningful concept chains and evidence trails across multiple documents that potentially connect them. These functionalities are implemented using an interactive visualization paradigm which assists users for a better understanding and interpretation of discovered hypotheses, and matching their domain knowledge with the algorithmic power of text mining techniques.
Finding unusual review patterns using unexpected rules
Authors: Nitin Jindal, Bing Liu, Ee-Peng Lim
Venue: CIKM
Year: 2010

In recent years, opinion mining attracted a great deal of research attention. However, limited work has been done on detecting opinion spam (or fake reviews). The problem is analogous to spam in Web search [1, 9 11]. However, review spam is harder to detect because it is very hard, if not impossible, to recognize fake reviews by manually reading them [2]. This paper deals with a restricted problem, i.e., identifying unusual review patterns which can represent suspicious behaviors of reviewers. We formulate the problem as finding unexpected rules. The technique is domain independent. Using the technique, we analyzed an Amazon.com review dataset and found many unexpected rules and rule groups which indicate spam activities.
Novel local features with hybrid sampling technique for image retrieval
Authors: Leszek Kaliciak, Dawei Song, Nirmalie Wiratunga, Jeff Z. Pan
Venue: CIKM
Year: 2010

In image retrieval, most existing approaches that incorporate local features produce high dimensional vectors, which lead to a high computational and data storage cost. Moreover, when it comes to the retrieval of generic real-life images, randomly generated patches are often more discriminant than the ones produced by corner/blob detectors. In order to tackle these problems, we propose a novel method incorporating local features with a hybrid sampling (a combination of detector-based and random sampling). We take three large data collections for the evaluation: MIRFlickr, ImageCLEF, and a collection from British National Geological Survey. The overall performance of the proposed approach is better than the performance of global features and comparable with the current state-of-the-art methods in content-based image retrieval. One of the advantages of our method when compared with others is its easy implementation and low computational cost. Another is that hybrid sampling can improve the performance of other methods based on the ``bag of visual words'' approach.
Incorporating terminology evolution for query translation in text retrieval with association rules
Authors: Amal Chaminda Kaluarachchi, Aparna S. Varde, Srikanta J. Bedathur, Gerhard Weikum, Jing Peng, Anna Feldman
Venue: CIKM
Year: 2010

Time-stamped documents such as newswire articles, blog posts and other web-pages are often archived online. When these archives cover long spans of time, the terminology within them could undergo significant changes. Hence, when users pose queries pertaining to historical information, over such documents, the queries need to be translated, taking into account these temporal changes, to provide accurate responses to users. For example, a query on Sri Lanka should automatically retrieve documents with its former name Ceylon. We call such concepts SITACs, i.e., Semantically Identical Temporally Altering Concepts. In order to discover SITACs, we propose an approach based on a novel framework constituting an integration of natural language processing, association rule mining, and contextual similarity as a learning technique. The proposed approach has been experimented with real data and has been found to yield good results with respect to efficiency and accuracy.
A new mathematics retrieval system
Authors: Shahab Kamali, Frank Wm. Tompa
Venue: CIKM
Year: 2010

The Web contains a large collection of documents, some with mathematical expressions. Because mathematical expressions are objects with complex structures and rather few distinct symbols, conventional text retrieval systems are not very successful in mathematics retrieval. The lack of a definition for similarity between mathematical expressions, and the inadequacy of searching for exact matches only, makes the problem of mathematics retrieval even harder. As a result, the few existing mathematics retrieval systems are not very helpful in addressing users' needs.
Identifying hotspots on the real-time web
Authors: Krishna Yeswanth Kamath, James Caverlee
Venue: CIKM
Year: 2010

We study the problem of automatically identifying ``hotspots'' on the real-time web. Concretely, we propose to identify highly-dynamic ad-hoc collections of users -- what we refer to as crowds -- in massive social messaging systems like Twitter and Facebook. The proposed approach relies on a message-based communication clustering approach over time-evolving graphs that captures the natural conversational nature of social messaging systems. One of the salient features of the proposed approach is an efficient locality-based clustering approach for identifying crowds of users in near real-time compared to more heavyweight static clustering algorithms. Based on a three month snapshot of Twitter consisting of 711,612 users and 61.3 million messages, we show how the proposed approach can efficiently and effectively identify Twitter-based crowds relative to static graph clustering techniques at a fraction of the computational cost.
Outcome aware ranking in interaction networks
Authors: Sampath Kameshwaran, Vinayaka Pandit, Sameep Mehta, Nukala Viswanadham, Kashyap Dixit
Venue: CIKM
Year: 2010

In this paper, we present a novel ranking technique that we developed in the context of an application that arose in a Service Delivery setting. We consider the problem of ranking agents of a service organization. The service agents typically need to interact with other service agents to accomplish the end goal of resolving customer requests. Their ranking needs to take into account two aspects: firstly, their importance in the network structure that arises as a result of their interactions, and secondly, the value generated by the interactions involving them. We highlight several other applications which have the common theme of ranking the participants of a value creation process based on the network structure of their interactions and the value generated by their interactions. We formally present the problem and describe the modeling technique which enables us to encode the value of interaction in the graph. Our ranking algorithm is based on extension of eigen value methods. We present experimental results on real-life, public domain datasets from the Internet Movie DataBase. This makes our experiments replicable and verifiable.
Third workshop on exploiting semantic annotations in information retrieval (ESAIR): CIKM 2010 workshop
Authors: Jaap Kamps, Jussi Karlgren, Ralf Schenkel
Venue: CIKM
Year: 2010

There is an increasing amount of structure on the Web as a result of modern Web languages, user tagging and annotation, and emerging robust NLP tools. These meaningful, semantic, annotations hold the promise to significantly enhance information access, by enhancing the depth of analysis of today's systems. Currently, we have only started exploring the possibilities and only begin to understand how these valuable semantic cues can be put to fruitful use. Unleashing the potential of semantic annotations requires us to think outside the box, by combining the insights of natural language processing (NLP) to go beyond bags of words, the insights of databases (DB) to use structure efficiently even when aggregating over millions of records, the insights of information retrieval (IR) in effective goal-directed search and evaluation, and the insights of knowledge management (KM) to get grips on the greater whole.