OpenRaiser/Intern-Atlas huggingface dataset:
Agro-Gator: digesting experts, logs, and N-grams
Authors: Michael Huggett
Venue: SIGIR
Year: 2010

As research includes more and larger user studies, a significant problem lies in combining the many types of data files into a single table suitable for analysis by common statistical tools.
Si-Fi: interactive similar item finder
Authors: Inbeom Hwang, Minsuk Kahng, Sung Eun Park, Jinwook Seo, Sang-goo Lee
Venue: SIGIR
Year: 2010

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A framework for BM25F-based XML retrieval
Authors: Kelly Y. Itakura, Charles L. A. Clarke
Venue: SIGIR
Year: 2010

We evaluate a framework for BM25F-based XML element retrieval. The framework gathers contextual information associated with each XML element into an associated field, which we call a characteristic field. The contents of the element and the contents of the characteristic field are then treated as distinct fields for BM25F weighting purposes. Evidence supporting this framework is drawn from both our own experiments and experiments reported in related work.
Search and browse log mining for web information retrieval: challenges, methods, and applications
Authors: Daxin Jiang, Jian Pei, Hang Li
Venue: SIGIR
Year: 2010

Huge amounts of search log data have been accumulated in various search engines. Currently, a commercial search engine receives billions of queries and collects tera-bytes of log data on any single day. Other than search log data, browse logs can be collected by client-side browser plug-ins, which record the browse information if users' permissions are granted. Such massive amounts of search/browse log data, on the one hand, provide great opportunities to mine the wisdom of crowds and improve search results as well as online advertisement. On the other hand, designing effective and efficient methods to clean, model, and process large scale log data also presents great challenges.
Visual summarization of web pages
Authors: Binxing Jiao, Linjun Yang, Jizheng Xu, Feng Wu
Venue: SIGIR
Year: 2010

Visual summarization is an attractive new scheme to summarize web pages, which can help achieve a more friendly user experience in search and re-finding tasks by allowing users quickly get the idea of what the web page is about and helping users recall the visited web page. In this paper, we perform a careful study on the recently proposed visual summarization approaches, including the thumbnail of the web page snapshot, the internal image in the web page which is representative of the content in the page, and the visual snippet which is a synthesized image based on the internal image, the title, and the logo found in the web page. Moreover, since the internal image based summarization approach hardly works when the representative internal images are unavailable, we propose a new strategy, which retrieves the representative image from the external to summarize the web page. The experimental results suggest that the various summarization approaches have respective advantages on different types of web pages. While internal images and thumbnails can provide a reliable summarization on web pages with dominant images and web pages with simple structure respectively, the external images are regarded as a useful information to complement the internal images and are demonstrated very useful in helping users understanding new web pages. The visual snippet performs well on the re-finding tasks since it incorporates the title and logo which are advantageous on identifying the visited web pages.
iCollaborate: harvesting value from enterprise web usage
Authors: Ajinkya Kale, Thomas Burris, Bhavesh Shah, T. L. Prasanna Venkatesan, Lakshmanan Velusamy, Manish Gupta, Melania Degerattu
Venue: SIGIR
Year: 2010

We are in a phase of 'Participatory Web' in which users add value' to the information on the web by publishing, tagging and sharing. The Participatory Web has enormous potential for an enterprise because unlike the users of the internet an enterprise is a community that shares common goals, assumptions, vocabulary and interest and has reliable user identification and mutual trust along with a central governance and incentives to collaborate. Everyday, the employees of an organization locate content relevant to their work on the web. Finding this information takes time, expertise and creativity, which costs an organization money. That is, the web pages employees find are knowledge assets owned by the enterprise. This investment in web-based knowledge assets is lost every time the enterprise fails to capture and reuse them. iCollaborate is tooled to capture user's web interaction, persist and analyze it, and feed that interaction back into the community - the enterprise.
Score distribution models: assumptions, intuition, and robustness to score manipulation
Authors: Evangelos Kanoulas, Keshi Dai, Virgiliu Pavlu, Javed A. Aslam
Venue: SIGIR
Year: 2010

Inferring the score distribution of relevant and non-relevant documents is an essential task for many IR applications (e.g. information filtering, recall-oriented IR, meta-search, distributed IR). Modeling score distributions in an accurate manner is the basis of any inference. Thus, numerous score distribution models have been proposed in the literature. Most of the models were proposed on the basis of empirical evidence and goodness-of-fit. In this work, we model score distributions in a rather different, systematic manner. We start with a basic assumption on the distribution of terms in a document. Following the transformations applied on term frequencies by two basic ranking functions, BM25 and Language Models, we derive the distribution of the produced scores for all documents. Then we focus on the relevant documents. We detach our analysis from particular ranking functions. Instead, we consider a model for precision-recall curves, and given this model, we present a general mathematical framework which, given any score distribution for all retrieved documents, produces an analytical formula for the score distribution of relevant documents that is consistent with the precision-recall curves that follow the aforementioned model. In particular, assuming a Gamma distribution for all retrieved documents, we show that the derived distribution for the relevant documents resembles a Gaussian distribution with a heavy right tail.
Linking wikipedia to the web
Authors: Rianne Kaptein, Pavel Serdyukov, Jaap Kamps
Venue: SIGIR
Year: 2010

We investigate the task of finding links from Wikipedia pages to external web pages. Such external links significantly extend the information in Wikipedia with information from the Web at large, while retaining the encyclopedic organization of Wikipedia. We use a language modeling approach to create a full-text and anchor text runs, and experiment with different document priors. In addition we explore whether social bookmarking site Delicious can be exploited to further improve our performance. We have constructed a test collection of 53 topics, which are Wikipedia pages on different entities. Our findings are that the anchor text index is a very effective method to retrieve home pages. Url class and anchor text length priors and their combination leads to the best results. Using Delicious on its own does not lead to very good results, but it does contain valuable information. Combining the best anchor text run and the Delicious run leads to further improvements.
A method to automatically construct a user knowledge model in a forum environment
Authors: Ahmad Kardan, Mehdi Garakani, Bamdad Bahrani
Venue: SIGIR
Year: 2010

Having a mechanism to validate the opinions and to identify experts in a forum could help people to favor one opinion against another. To achieve this, some solutions have already been introduced, including social network analysis techniques and reputation modeling. However, neither of these solutions considers the users' knowledge to identify an expert. In this paper, a novel method is proposed which estimates users' knowledge based on the forum itself, and identifies the possible areas of expertise associated with each user.
Estimation of statistical translation models based on mutual information for ad hoc information retrieval
Authors: Maryam Karimzadehgan, ChengXiang Zhai
Venue: SIGIR
Year: 2010

As a principled approach to capturing semantic relations of words in information retrieval, statistical translation models have been shown to outperform simple document language models which rely on exact matching of words in the query and documents. A main challenge in applying translation models to ad hoc information retrieval is to estimate a translation model without training data. Existing work has relied on training on synthetic queries generated based on a document collection. However, this method is computationally expensive and does not have a good coverage of query words. In this paper, we propose an alternative way to estimate a translation model based on normalized mutual information between words, which is less computationally expensive and has better coverage of query words than the synthetic query method of estimation. We also propose to regularize estimated translation probabilities to ensure sufficient probability mass for self-translation. Experiment results show that the proposed mutual information-based estimation method is not only more efficient, but also more effective than the synthetic query-based method, and it can be combined with pseudo-relevance feedback to further improve retrieval accuracy. The results also show that the proposed regularization strategy is effective and can improve retrieval accuracy for both synthetic query-based estimation and mutual information-based estimation.
Serendipitous recommendations via innovators
Authors: Noriaki Kawamae
Venue: SIGIR
Year: 2010

To realize services that provide serendipity, this paper assesses the surprise of each user when presented recommendations. We propose a recommendation algorithm that focuses on the search time that, in the absence of any recommendation, each user would need to find a desirable and novel item by himself. Following the hypothesis that the degree of user's surprise is proportional to the estimated search time, we consider both innovators' preferences and trends for identifying items with long estimated search times. To predict which items the target user is likely to purchase in the near future, the candidate items, this algorithm weights each item that innovators have purchased and that reflect one or more current trends; it then lists them in order of decreasing weight. Experiments demonstrate that this algorithm outputs recommendations that offer high user/item coverage, a low Gini coefficient, and long estimated search times, and so offers a high degree of recommendation serendipitousness.
Author interest topic model
Authors: Noriaki Kawamae
Venue: SIGIR
Year: 2010

This paper presents a hierarchical topic model that simultaneously captures topics and author's interests. Our proposal, the Author Interest Topic model (AIT), introduces a latent variable with a separate probability distribution over topics into each document. Experiments on a research paper corpus show that the AIT is useful as a generative model.
Scalability of findability: effective and efficient IR operations in large information networks
Authors: Weimao Ke, Javed Mostafa
Venue: SIGIR
Year: 2010

It is crucial to study basic principles that support adaptive and scalable retrieval functions in large networked environments such as the Web, where information is distributed among dynamic systems. We conducted experiments on decentralized IR operations on various scales of information networks and analyzed effectiveness, efficiency, and scalability of various search methods. Results showed network structure, i.e., how distributed systems connect to one another, is crucial for retrieval performance. Relying on partial indexes of distributed systems, some level of network clustering enabled very efficient and effective discovery of relevant information in large scale networks. For a given network clustering level, search time was well explained by a poly-logarithmic relation to network size (i.e., the number of distributed systems), indicating a high scalability potential for searching in a growing information space. In addition, network clustering only involved local self-organization and required no global control - clustering time remained roughly constant across the various scales of networks.
Investigation on smoothing and aggregation methods in blog retrieval
Authors: Mostafa Keikha
Venue: SIGIR
Year: 2010

Recently, user generated data is growing rapidly and becoming one of the most important source of information in the web. Blogosphere (the collection of blogs on the web) is one of the main source of information in this category. In my work for my PhD, I mainly focussed on the blog distillation task which is: given a user query find the blogs that are most related to the query topic [3].
Ranking using multiple document types in desktop search
Authors: Jin Young Kim, W. Bruce Croft
Venue: SIGIR
Year: 2010

A typical desktop environment contains many document types (email, presentations, web pages, pdfs, etc.) each with different metadata. Predicting which types of documents a user is looking for in the context of a given query is a crucial part of providing effective desktop search. The problem is similar to selecting resources in distributed IR, but there are some important differences.
MEMOSE: search engine for emotions in multimedia documents
Authors: Kathrin Knautz, Tobias Siebenlist, Wolfgang G. Stock
Venue: SIGIR
Year: 2010

The MEMOSE (Media Emotion Search) system is a specialized search engine for fundamental emotions in all kinds of emotional-laden documents. We apply a controlled vocabulary for basic emotions, a slide control to adjust the intensities of the emotions and the approach of broad folksonomies. The paper describes the indexing and the retrieval tool of MEMOSE and results from its evaluation.
The impact of collection size on relevance and diversity
Authors: Marijn Koolen, Jaap Kamps
Venue: SIGIR
Year: 2010

It has been observed that precision increases with collection size. One explanation could be that the redundancy of information increases, making it easier to find multiple documents conveying the same information. Arguably, a user has no interest in reading the same information over and over, but would prefer a set of diverse search results covering multiple aspects of the search topic. In this paper, we look at the impact of the collection size on the relevance and diversity of retrieval results by down-sampling the collection.
Multilingual people search
Authors: Shaishav Kumar, Raghavendra Udupa
Venue: SIGIR
Year: 2010

People Search is an important search service with multiple applications (eg. looking up a friend on Facebook, finding colleagues in corporate email directories etc). With the proportion of non-English users on a steady rise, people search services are being used by users from diverse language demographics. Users may issue name search queries against these directories in languages other than the language of the directory, in which case the present monolingual name search approaches will not work. In this demo, we present a Multilingual People Search system capable of performing fast name lookups on large user directories, independent of the directory language. Our system has applications in areas like social networking, enterprise search and email address book search.
Introduction to probabilistic models in IR
Authors: Victor Lavrenko
Venue: SIGIR
Year: 2010

Most of today's state-of-the-art retrieval models, including BM25 and language modeling, are grounded in probabilistic principles. Having a working understanding of these principles can help researchers understand existing retrieval models better and also provide industrial practitioners with an understanding of how such models can be applied to real world problems.
To translate or not to translate?
Authors: Chia-Jung Lee, Chin-Hui Chen, Shao-Hang Kao, Pu-Jen Cheng
Venue: SIGIR
Year: 2010

Query translation is an important task in cross-language information retrieval (CLIR) aiming to translate queries into languages used in documents. The purpose of this paper is to investigate the necessity of translating query terms, which might differ from one term to another. Some untranslated terms cause irreparable performance drop while others do not. We propose an approach to estimate the translation probability of a query term, which helps decide if it should be translated or not. The approach learns regression and classification models based on a rich set of linguistic and statistical properties of the term. Experiments on NTCIR-4 and NTCIR-5 English-Chinese CLIR tasks demonstrate that the proposed approach can significantly improve CLIR performance. An in-depth analysis is also provided for discussing the impact of untranslated out-of-vocabulary (OOV) query terms and translation quality of non-OOV query terms on CLIR performance.
Uncovering social spammers: social honeypots + machine learning
Authors: Kyumin Lee, James Caverlee, Steve Webb
Venue: SIGIR
Year: 2010

Web-based social systems enable new community-based opportunities for participants to engage, share, and interact. This community value and related services like search and advertising are threatened by spammers, content polluters, and malware disseminators. In an effort to preserve community value and ensure longterm success, we propose and evaluate a honeypot-based approach for uncovering social spammers in online social systems. Two of the key components of the proposed approach are: (1) The deployment of social honeypots for harvesting deceptive spam profiles from social networking communities; and (2) Statistical analysis of the properties of these spam profiles for creating spam classifiers to actively filter out existing and new spammers. We describe the conceptual framework and design considerations of the proposed approach, and we present concrete observations from the deployment of social honeypots in MySpace and Twitter. We find that the deployed social honeypots identify social spammers with low false positive rates and that the harvested spam data contains signals that are strongly correlated with observable profile features (e.g., content, friend information, posting patterns, etc.). Based on these profile features, we develop machine learning based classifiers for identifying previously unknown spammers with high precision and a low rate of false positives.
Mining the blogosphere for top news stories identification
Authors: Yeha Lee, Hun-Young Jung, Woosang Song, Jong-Hyeok Lee
Venue: SIGIR
Year: 2010

The analysis of query logs from blog search engines show that news-related queries occupy a significant portion of the logs. This raises a interesting research question on whether the blogosphere can be used to identify important news stories. In this paper, we present novel approaches to identify important news story headlines from the blogosphere for a given day. The proposed system consists of two components based on the language model framework, the query likelihood and the news headline prior. For the query likelihood, we propose several approaches to estimate the query language model and the news headline language model. We also suggest several criteria to evaluate the news headline prior that is the prior belief about the importance or newsworthiness of the news headline for a given day. Experimental results show that our system significantly outperforms a baseline system. Specifically, the proposed approach gives 2.62% and 10.19% further increases in MAP and P@5 over the best performing result of the TREC&apos;09 Top Stories Identification Task.
High precision opinion retrieval using sentiment-relevance flows
Authors: Seung-Wook Lee, Jung-Tae Lee, Young-In Song, Hae-Chang Rim
Venue: SIGIR
Year: 2010

Opinion retrieval involves the measuring of opinion score of a document about the given topic. We propose a new method, namely sentiment-relevance flow, that naturally unifies the topic relevance and the opinionated nature of a document. Experiments conducted over a large-scaled Web corpus show that the proposed approach improves performance of opinion retrieval in terms of precision at top ranks.
Information retrieval for e-discovery
Authors: David D. Lewis
Venue: SIGIR
Year: 2010

Discovery, the process under which parties to legal cases must reveal documents relevant to the disputed issues is a core aspect of trials in the United States, and a lesser but important factor in other countries. Discovery on documents stored in computerized systems (known variously as electronic discovery, e-discovery, e-disco, EDD, and ED) is increasingly the major factor in discovery, and has become a multi-billion dollar industry.
HCC: a hierarchical co-clustering algorithm
Authors: Jingxuan Li, Tao Li
Venue: SIGIR
Year: 2010

In this poster, we develop a novel method, called HCC, for hierarchical co-clustering. HCC brings together two interrelated but distinct themes from clustering: hierarchical clustering and co-clustering.
Robust audio identification for MP3 popular music
Authors: Wei Li, Yaduo Liu, Xiangyang Xue
Venue: SIGIR
Year: 2010

Audio identification via fingerprint has been an active research field with wide applications for years. Many technical papers were published and commercial software systems were also employed. However, most of these previously reported methods work on the raw audio format in spite of the fact that nowadays compressed format audio, especially MP3 music, has grown into the dominant way to store on personal computers and transmit on the Internet. It would be interesting if a compressed unknown audio fragment is able to be directly recognized from the database without the fussy and time-consuming decompression-identification-recompression procedure. So far, very few algorithms run directly in the compressed domain for music information retrieval, and most of them take advantage of MDCT coefficients or derived energy type of features. As a first attempt, we propose in this paper utilizing compressed-domain spectral entropy as the audio feature to implement a novel audio fingerprinting algorithm. The compressed songs stored in a music database and the possibly distorted compressed query excerpts are first partially decompressed to obtain the MDCT coefficients as the intermediate result. Then by grouping granules into longer blocks, remapping the MDCT coefficients into 192 new frequency lines to unify the frequency distribution of long and short windows, and defining 9 new subbands which cover the main frequency bandwidth of popular songs in accordance with the scale-factor bands of short windows, we calculate the spectral entropy of all consecutive blocks and come to the final fingerprint sequence by means of magnitude relationship modeling. Experiments show that such fingerprints exhibit strong robustness against various audio signal distortions like recompression, noise interference, echo addition, equalization, band-pass filtering, pitch shifting, and slight time-scale modification etc. For 5s-long query examples which might be severely degraded, an average top-five retrieval precision rate of more than 90% can be obtained in our test data set composed of 1822 popular songs.
Robust music identification based on low-order zernike moment in the compressed domain
Authors: Wei Li, Yaduo Liu, Xiangyang Xue
Venue: SIGIR
Year: 2010

In this paper, we devise a novel robust music identification algorithm utilizing compressed-domain audio Zernike moment adapted from image processing techniques as the pivotal feature. Audio fingerprint derived from this feature exhibits strong robustness against various audio signal distortions including the challenging pitch shifting and time-scale modification. Experiments show that in our test dataset composed of 1822 popular songs, a 5s music query example which might have been severely corrupted is still sufficient to identify its original near-duplicate copy, with more than 90% top five precision rate.