OpenRaiser/Intern-Atlas huggingface dataset:
Out of sight, not out of mind: on the effect of social and physical detachment on information need
Authors: Elad Yom-Tov, Fernando Diaz
Venue: SIGIR
Year: 2011

The information needs of users and the documents which answer it are frequently contingent on the different characteristics of users. This is especially evident during natural disasters, such as earthquakes and violent weather incidents, which create a strong transient information need. In this paper we investigate how the information need of users is affected by their physical detachment, as estimated by their physical location in relation to that of the event, and by their social detachment, as quantified by the number of their acquaintances who may be affected by the event. Drawing on large-scale data from three major events, we show that social and physical detachment levels of users are a major influence on their information needs, as manifested by their search engine queries. We demonstrate how knowing social and physical detachment levels can assist in improving retrieval for two applications: identifying search queries related to events and ranking results in response to event-related queries. We find that the average precision in identifying relevant search queries improves by approximately 18%, and that the average precision of ranking that uses detachment information improves by 10%.
Location and timeliness of information sources during news events
Authors: Elad Yom-Tov, Fernando Diaz
Venue: SIGIR
Year: 2011

People nowadays can obtain information on current news events through media outlets, social media, and by actively seeking information using search engines. In this paper we investigate the temporal relationship between news coverage by media outlets, social media, and query logs and show that social media frequently precedes other information sources. Additionally, we demonstrate that there is strong negative correlation between the probability for reporting of an event and the distance of the information source from the event.
Predicting eBay listing conversion
Authors: Ted Tao Yuan, Zhaohui Chen, Mike Mathieson
Venue: SIGIR
Year: 2011

At eBay Market Place, listing conversion rate can be measured by number of items sold divided by number of items in a sample set. For a given item, conversion rate can also be treated as the probability of sale. By investigating eBay listings' transactional patterns, as well as item attributes and user click-through data, we developed conversion models that allow us to predict a live listing's probability of sale. In this paper, we discuss the design and implementation of such conversion models. These models are highly valuable in analysis of inventory quality and ranking. Our work reveals the uniqueness of sales-oriented search at eBay and its similarity to general web search problems.
Beyond search: statistical topic models for text analysis
Authors: ChengXiang Zhai
Venue: SIGIR
Year: 2011

Search is generally a means to the end of finishing a task. While the current search engines are useful to users for finding relevant information, they offer little help to users for further digesting and analyzing the overwhelming found information needed for finishing a complex task. In this talk, I will discuss how statistical topic models can be used to help users analyze and digest the found relevant information and turn search results into actionable knowledge needed to complete a task. I will present several general statistical topic models for extracting and analyzing topics and their patterns in text, and show sample applications of such models in tasks such as opinion integration, comparative summarization, contextual topic trend analysis, and event impact analysis. The talk will conclude with a discussion of novel challenges raised in extending a search engine to an analysis engine that can go beyond search to provide more complete support for users to finish their tasks.
Predicting users' domain knowledge from search behaviors
Authors: Xiangmin Zhang, Michael J. Cole, Nicholas J. Belkin
Venue: SIGIR
Year: 2011

This study uses regression modeling to predict a user's domain knowledge level (DK) from implicit evidence provided by certain search behaviors. A user study (n=35) with recall-oriented search tasks in the genomic domain was conducted. A number of regression models of a person's DK, were generated using different behavior variable selection methods. The best model highlights three behavior variables as DK predictors: the number of documents saved, the average query length, and the average ranking position of documents opened. The model is validated using the split sampling method. Limitations and future research directions are discussed.
How to count thumb-ups and thumb-downs?: an information retrieval approach to user-rating based ranking of items
Authors: Dell Zhang, Robert Mao, Haitao Li, Joanne Mao
Venue: SIGIR
Year: 2011

It is a common practice among Web 2.0 services to allow users to rate items on their sites. In this paper, we first point out the flaws of the popular methods for user-rating based ranking of items, and then argue that two well-known Information Retrieval (IR) techniques, namely the Probability Ranking Principle and Statistical Language Modelling, provide a simple but effective solution to this problem.
Document clustering with universum
Authors: Dan Zhang, Jingdong Wang, Luo Si
Venue: SIGIR
Year: 2011

Document clustering is a popular research topic, which aims to partition documents into groups of similar objects (i.e., clusters), and has been widely used in many applications such as automatic topic extraction, document organization and filtering. As a recently proposed concept, Universum is a collection of "non-examples" that do not belong to any concept/cluster of interest. This paper proposes a novel document clustering technique -- Document Clustering with Universum, which utilizes the Universum examples to improve the clustering performance. The intuition is that the Universum examples can serve as supervised information and help improve the performance of clustering, since they are known not belonging to any meaningful concepts/clusters in the target domain. In particular, a maximum margin clustering method is proposed to model both target examples and Universum examples for clustering. An extensive set of experiments is conducted to demonstrate the effectiveness and efficiency of the proposed algorithm.
Filtering semi-structured documents based on faceted feedback
Authors: Lanbo Zhang, Yi Zhang, Qianli Xing
Venue: SIGIR
Year: 2011

Existing adaptive filtering systems learn user profiles based on users' relevance judgments on documents. In some cases, users have some prior knowledge about what features are important for a document to be relevant. For example, a Spanish speaker may only want news written in Spanish, and thus a relevant document should contain the feature "Language: Spanish"; a researcher working on HIV knows an article with the medical subject "Subject: AIDS" is very likely to be interesting to him/her.
CRTER: using cross terms to enhance probabilistic information retrieval
Authors: Jiashu Zhao, Jimmy Xiangji Huang, Ben He
Venue: SIGIR
Year: 2011

Term proximity retrieval rewards a document where the matched query terms occur close to each other. Although term proximity is known to be effective in many Information Retrieval (IR) applications, the within-document distribution of each individual query term and how the query terms associate with each other, are not fully considered. In this paper, we introduce a pseudo term, namely Cross Term, to model term proximity for boosting retrieval performance. An occurrence of a query term is assumed to have an impact towards its neighboring text, which gradually weakens with the increase of the distance to the place of occurrence. We use a shape function to characterize such an impact. A Cross Term occurs when two query terms appear close to each other and their impact shape functions have an intersection. We propose a Cross Term Retrieval (CRTER) model that combines the Cross Terms' information with basic probabilistic weighting models to rank the retrieved documents. Extensive experiments on standard TREC collections illustrate the effectiveness of our proposed CRTER model.
SEJoin: an optimized algorithm towards efficient approximate string searches
Authors: Junfeng Zhou, Ziyang Chen, Jingrong Zhang
Venue: SIGIR
Year: 2011

We investigated the problem of finding from a collection of strings those similar to a given query string based on edit distance, for which the critical operation is merging inverted lists of grams generated from the collection of strings. We present an efficient algorithm to accelerate the merging operation.
OrientSTS: spatio-temporal sequence searching in flickr
Authors: Chunjie Zhou, Dongqi Liu, Xiaofeng Meng
Venue: SIGIR
Year: 2011

Nowadays, due to the increasing user requirements of efficient and personalized services, a perfect travel plan is urgently needed. However, at present it is hard for people to make a personalized traveling plan. Most of them follow other people's general travel trajectory. So only after finishing their travel, do they know which scene is their favorite, which is not, and what is the perfect order of visits. In this research we propose a novel spatio-temporal sequence (STS) searching, which mainly includes two steps.
Location disambiguation for geo-tagged images
Authors: Zhu Zhu, Lidan Shou, Kuang Mao, Gang Chen
Venue: SIGIR
Year: 2011

In this poster, we address the problem of location disambiguation for geotagged Web photo resources. We propose an approach for analyzing and partitioning large geotagged photo collections using geographic and semantic information. By organizing the dataset in a structural scheme, we resolve the location ambiguity and clutter problem yield by massive volume of geotagged photos.
The interactive PRP for diversifying document rankings
Authors: Guido Zuccon, Leif Azzopardi, C. J. van Rijsbergen
Venue: SIGIR
Year: 2011

The assumptions underlying the Probability Ranking Principle (PRP) have led to a number of alternative approaches that cater or compensate for the PRP's limitations. In this poster we focus on the Interactive PRP (iPRP), which rejects the assumption of independence between documents made by the PRP. Although the theoretical framework of the iPRP is appealing, no instantiation has been proposed and investigated. In this poster, we propose a possible instantiation of the principle, performing the first empirical comparison of the iPRP against the PRP. For document diversification, our results show that the iPRP is significantly better than the PRP, and comparable to or better than other methods such as Modern Portfolio Theory.
To index or not to index: time-space trade-offs in search engines with positional ranking functions
Authors: Diego Arroyuelo, Senén González, Mauricio Marín, Mauricio Oyarzún, Torsten Suel
Venue: SIGIR
Year: 2012

Positional ranking functions, widely used in Web search engines, improve result quality by exploiting the positions of the query terms within documents. However, it is well known that positional indexes demand large amounts of extra space, typically about three times the space of a basic nonpositional index. Textual data, on the other hand, is needed to produce text snippets. In this paper, we study time-space trade-offs for search engines with positional ranking functions and text snippet generation. We consider both index-based and non-index based alternatives for positional data. We aim to answer the question of whether one should index positional data or not. We show that there is a wide range of practical time-space trade-offs. Moreover, we show that both position and textual data can be stored using about 71% of the space used by traditional positional indexes, with a minor increase in query time. This yields considerable space savings and outperforms, both in space and time, recent alternatives from the literature. We also propose several efficient compressed text representations for snippet generation, which are able to use about half of the space of current state-of-the-art alternatives with little impact in query processing time.
ALF: a client side logger and server for capturing user interactions in web applications
Authors: Leif Azzopardi, Myles Doolan, Richard Glassey
Venue: SIGIR
Year: 2012

This demonstration paper introduces ALF which provides a light-weight client side logging application and a server for collecting user interaction data. ALF has been designed as a loosely coupled independent service that runs in parallel with the IR web application that requires logging
YooSee: a video browsing application for young children
Authors: Leif Azzopardi, Douglas Dowie, Kelly Ann Marshall
Venue: SIGIR
Year: 2012

Nowadays children as young as two years old can easily interact with mobile touch screen devices and personal computers to watch online videos through services such as YouTube. However, such services present a number of challenges for young children (e.g. fine grain gestures/interactions and good typing/literacy skills). In addition, when children use such services there is a risk that they may stumble upon content that is inappropriate. YooSee is a web-based application developed using the PuppyIR framework and designed for children aged between two and six years old. YooSee enables children to: (1) search and browse through video content using an engaging, novel interaction paradigm, and (2) be able to safely enjoy moderated video content.
MaSe: create your own mash-up search interface
Authors: Leif Azzopardi, Douglas Dowie, Kelly Ann Marshall, Richard Glassey
Venue: SIGIR
Year: 2012

MaSe provides a sandbox environment for high school students to create their own personalised search interface. It has been designed with two major goals in mind: (1) as a hands-on tutorial for school children, to excite them about programming and computing science through the development of a practical application, and (2) to enable children to design and create their own search interface without extensive programming knowledge or prior experience. Consequently, MaSe provides a way to ascertain what children would like from a search engine interface in an exploratory and creative way as they can create a working prototype. This approach contrasts with previous work on exploring children's requirements of IR systems which attempts to directly elicit user needs through more traditional methods (i.e. surveys, interviews, focus groups, etc). However, we have attempted to incorporate the design guidelines for children as identified by Large (2006) into MaSe, where: we make use of bright colours, large text fonts, spell checking and the use of icons to represent search services, as well as including a thematic experience as suggested by Large (2006), with the use of a puppy avatar and puppy dog footprints.
PageFetch: a retrieval game for children (and adults)
Authors: Leif Azzopardi, James Purvis, Richard Glassey
Venue: SIGIR
Year: 2012

Children often struggle with information retrieval tasks as searching for information often requires a developed vocabulary and strong categorisation skills; neither of which are particularly developed in children under the age of 12. In a study conducted by Druin et al, it was found that in an experimental setting many children are often uninterested in searching for information online or are only interested in searching for information that is relevant to their personal interests. Consequently, children who were unmotivated were the least successful in completing information retrieval tasks in their study. It was suggested that a more effective means of engaging child participants in search studies must be developed in order to gain further insights into the searching behaviours of children. To this end we have developed a game called PageFetch which aims to engage children (aged 8 to 80) in completing search tasks through a fun and interactive search-like interface.
Adaptive IR for exploratory search support
Authors: Daniel T. J. Backhausen
Venue: SIGIR
Year: 2012

Most Information Retrieval (IR) software is designed to fit a general user where users are submitting queries and the retrieval system returns a ranked list of results. Regardless of the user, the query always returns the same list of results. Individual aspects like age, gender, profession or experience are often not taken into account, for example the difference in searching between children and adults. Although long challenged by works such as Bates' berrypicking model [1], common systems still assume that the user has a static information need which remains unchanged during the seeking process. Moreover many systems are strongly optimized for lookup searches, expecting that the user is only interested in facts and not in complex problem solving.
Automatic suggestion of query-rewrite rules for enterprise search
Authors: Zhuowei Bao, Benny Kimelfeld, Yunyao Li
Venue: SIGIR
Year: 2012

Enterprise search is challenging for several reasons, notably the dynamic terminology and jargon that are specific to the enterprise domain. This challenge is partly addressed by having domain experts maintaining the enterprise search engine and adapting it to the domain specifics. Those administrators commonly address user complaints about relevant documents missing from the top matches. For that, it has been proposed to allow administrators to influence search results by crafting query-rewrite rules, each specifying how queries of a certain pattern should be modified or augmented with additional queries. Upon a complaint, the administrator seeks a semantically coherent rule that is capable of pushing the desired documents up to the top matches. However, the creation and maintenance of rewrite rules is highly tedious and time consuming. Our goal in this work is to ease the burden on search administrators by automatically suggesting rewrite rules. This automation entails several challenges. One major challenge is to select, among many options, rules that are ``natural'' from a semantic perspective (e.g., corresponding to closely related and syntactically complete concepts). Towards that, we study a machine-learning classification approach. The second challenge is to accommodate the cross-query effect of rules---a rule introduced in the context of one query can eliminate the desired results for other queries and the desired effects of other rules. We present a formalization of this challenge as a generic computational problem. As we show that this problem is highly intractable in terms of complexity theory, we present heuristic approaches and optimization thereof. In an experimental study within IBM intranet search, those heuristics achieve near-optimal quality and well scale to large data sets.
Modeling higher-order term dependencies in information retrieval using query hypergraphs
Authors: Michael Bendersky, W. Bruce Croft
Venue: SIGIR
Year: 2012

Many of the recent, and more effective, retrieval models have incorporated dependencies between the terms in the query. In this paper, we advance this query representation one step further, and propose a retrieval framework that models higher-order term dependencies, i.e., dependencies between arbitrary query concepts rather than just query terms. In order to model higher-order term dependencies, we represent a query using a hypergraph structure -- a generalization of a graph, where a (hyper)edge connects an arbitrary subset of vertices. A vertex in a query hypergraph corresponds to an individual query concept, and a dependency between a subset of these vertices is modeled through a hyperedge. An extensive empirical evaluation using both newswire and web corpora demonstrates that query representation using hypergraphs is highly beneficial for verbose natural language queries. For these queries, query hypergraphs significantly improve the retrieval effectiveness of several state-of-the-art models that do not employ higher-order term dependencies.
A utility-theoretic ranking method for semi-automated text classification
Authors: Giacomo Berardi, Andrea Esuli, Fabrizio Sebastiani
Venue: SIGIR
Year: 2012

In Semi-Automated Text Classification (SATC) an automatic classifier F labels a set of unlabelled documents D, following which a human annotator inspects (and corrects when appropriate) the labels attributed by F to a subset D' of D, with the aim of improving the overall quality of the labelling. An automated system can support this process by ranking the automatically labelled documents in a way that maximizes the expected increase in effectiveness that derives from inspecting D. An obvious strategy is to rank D so that the documents that F has classified with the lowest confidence are top-ranked. In this work we show that this strategy is suboptimal. We develop a new utility-theoretic ranking method based on the notion of inspection gain, defined as the improvement in classification effectiveness that would derive by inspecting and correcting a given automatically labelled document. We also propose a new effectiveness measure for SATC-oriented ranking methods, based on the expected reduction in classification error brought about by partially inspecting a list generated by a given ranking method. We report the results of experiments showing that, with respect to the baseline method above, and according to the proposed measure, our ranking method can achieve substantially higher expected reductions in classification error.
Active query selection for learning rankers
Authors: Mustafa Bilgic, Paul N. Bennett
Venue: SIGIR
Year: 2012

Methods that reduce the amount of labeled data needed for training have focused more on selecting which documents to label than on which queries should be labeled. One exception to this (Long et al. 2010) uses expected loss optimization (ELO) to estimate which queries should be selected but is limited to rankers that predict absolute graded relevance. In this work, we demonstrate how to easily adapt ELO to work with any ranker and show that estimating expected loss in DCG is more robust than NDCG even when the final performance measure is NDCG.
Extending BM25 with multiple query operators
Authors: Roi Blanco, Paolo Boldi
Venue: SIGIR
Year: 2012

Traditional probabilistic relevance frameworks for informational retrieval refrain from taking positional information into account, due to the hurdles of developing a sound model while avoiding an explosion in the number of parameters. Nonetheless, the well-known BM25F extension of the successful Okapi ranking function can be seen as an embryonic attempt in that direction. In this paper, we proceed along the same line, defining the notion of virtual region: a virtual region is a part of the document that, like a BM25F-field, can provide a (larger or smaller, depending on a tunable weighting parameter) evidence of relevance of the document; differently from BM25F fields, though, virtual regions are generated implicitly by applying suitable (usually, but not necessarily, positional-aware) operators to the query. This technique fits nicely in the eliteness model behind BM25 and provides a principled explanation to BM25F; it specializes to BM25(F) for some trivial operators, but has a much more general appeal. Our experiments (both on standard collections, such as TREC, and on Web-like repertoires) show that the use of virtual regions is beneficial for retrieval effectiveness.