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Foundations and Trends® in Information Retrieval
Journal Prestige (SJR): 1.073
Citation Impact (citeScore): 10
Number of Followers: 105  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 1554-0669 - ISSN (Online) 1554-0677
Published by Now Publishers Inc Homepage  [30 journals]
  • Geographic Information Retrieval: Progress and Challenges in Spatial
           Search of Text
    • Abstract: Significant amounts of information available today containreferences to places on earth. Traditionally such informationhas been held as structured data and was the concern ofGeographic Information Systems (GIS). However, increasingamounts of data in the form of unstructured text areavailable for indexing and retrieval that also contain spatialreferences. This monograph describes the field of GeographicInformation Retrieval (GIR) that seeks to developspatially-aware search systems and support user’s geographicalinformation needs. Important concepts with respect tostoring, querying and analysing geographical information incomputers are introduced, before user needs and interactionin the context of GIR are explored. The task of associatingdocuments with coordinates, prior to their indexing andranking forms the core of any GIR system, and differentapproaches and their implications are discussed. Evaluatingthe resulting systems and their components, and differentparadigms for doing so continue to be an important areaof research in GIR and are illustrated through a number ofexamples. The article concludes by setting out a range offuture challenges for research in this field.Suggested CitationRoss S. Purves, Paul Clough, Christopher B. Jones, Mark H. Hall and Vanessa Murdock (2018), "Geographic Information Retrieval: Progress and Challenges in Spatial Search of Text", Foundations and Trends® in Information Retrieval: Vol. 12: No. 2-3, pp 164-318. http://dx.doi.org/10.1561/1500000034
      PubDate: Wed, 21 Feb 2018 00:00:00 +010
       
  • Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
    • Abstract: The most significant progress in recent years in online display advertisingis what is known as the Real-Time Bidding (RTB) mechanismto buy and sell ads. RTB essentially facilitates buying an individual adimpression in real time while it is still being generated from a user’svisit. RTB not only scales up the buying process by aggregating alarge amount of available inventories across publishers but, most importantly,enables direct targeting of individual users. As such, RTBhas fundamentally changed the landscape of digital marketing. Scientifically,the demand for automation, integration and optimisation inRTB also brings new research opportunities in information retrieval,data mining, machine learning and other related fields. In this monograph,an overview is given of the fundamental infrastructure, algorithms,and technical solutions of this new frontier of computationaladvertising. The covered topics include user response prediction, bidlandscape forecasting, bidding algorithms, revenue optimisation, statisticalarbitrage, dynamic pricing, and ad fraud detection.Suggested CitationJun Wang, Weinan Zhang and Shuai Yuan (2017), "Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting", Foundations and Trends® in Information Retrieval: Vol. 11: No. 4-5, pp 297-435. http://dx.doi.org/10.1561/1500000049
      PubDate: Mon, 24 Jul 2017 00:00:00 +020
       
  • Applications of Topic Models
    • Abstract: How can a single person understand what’s going on in a collection ofmillions of documents' This is an increasingly common problem: siftingthrough an organization’s e-mails, understanding a decade worth ofnewspapers, or characterizing a scientific field’s research. Topic modelsare a statistical framework that help users understand large documentcollections: not just to find individual documents but to understand thegeneral themes present in the collection.This survey describes the recent academic and industrial applicationsof topic models with the goal of launching a young researcher capableof building their own applications of topic models. In addition to topicmodels’ effective application to traditional problems like informationretrieval, visualization, statistical inference, multilingual modeling, andlinguistic understanding, this survey also reviews topic models’ abilityto unlock large text collections for qualitative analysis. We review theirsuccessful use by researchers to help understand fiction, non-fiction,scientific publications, and political texts.Suggested CitationJordan Boyd-Graber, Yuening Hu and David Mimno (2017), "Applications of Topic Models", Foundations and Trends® in Information Retrieval: Vol. 11: No. 2-3, pp 143-296. http://dx.doi.org/10.1561/1500000030
      PubDate: Thu, 20 Jul 2017 00:00:00 +020
       
  • Searching the Enterprise
    • Abstract: Search has become ubiquitous but that does not mean that search hasbeen solved. Enterprise search, which is broadly speaking the use ofinformation retrieval technology to find information within organisations,is a good example to illustrate this. It is an area that is of hugeimportance for businesses, yet has attracted relatively little academicinterest. This monograph will explore the main issues involved in enterprisesearch both from a research as well as a practical point of view.We will first plot the landscape of enterprise search and its links to relatedareas. This will allow us to identify key features before we surveythe field in more detail. Throughout the monograph we will discuss thetopic as part of the wider information retrieval research field, and weuse Web search as a common reference point as this is likely the searchapplication area that the average reader is most familiar with.Suggested CitationUdo Kruschwitz and Charlie Hull (2017), "Searching the Enterprise", Foundations and Trends® in Information Retrieval: Vol. 11: No. 1, pp 1-142. http://dx.doi.org/10.1561/1500000053
      PubDate: Wed, 12 Jul 2017 00:00:00 +020
       
  • Aggregated Search
    • Abstract: The goal of aggregated search is to provide integrated search acrossmultiple heterogeneous search services in a unified interface—a singlequery box and a common presentation of results. In the web searchdomain, aggregated search systems are responsible for integrating resultsfrom specialized search services, or verticals, alongside the coreweb results. For example, search portals such as Google, Bing, andYahoo! provide access to vertical search engines that focus on differenttypes of media (images and video), different types of search tasks(search for local businesses and online products), and even applicationsthat can help users complete certain tasks (language translation andmath calculations).Aggregated search systems perform two mains tasks. The first task(vertical selection) is to predict which verticals (if any) to present inresponse to a user’s query. The second task (vertical presentation) is topredict where and how to present each selected vertical alongside thecore web results.The goal of this work is to provide a comprehensive summary of previousresearch in aggregated search. We first describe why aggregatedsearch requires unique solutions. Then, we discuss different sources ofevidence that are likely to be available to an aggregated search system,as well as different techniques for integrating evidence in order to makevertical selection and presentation decisions. Next, we survey differentevaluation methodologies for aggregated search and discuss prioruser studies that have aimed to better understand how users behavewith aggregated search interfaces. Finally, we review different advancedtopics in aggregated search.Suggested CitationJaime Arguello (2017), "Aggregated Search", Foundations and Trends® in Information Retrieval: Vol. 10: No. 5, pp 365-502. http://dx.doi.org/10.1561/1500000052
      PubDate: Mon, 06 Mar 2017 00:00:00 +010
       
  • Web Forum Retrieval and Text Analytics: A Survey
    • Abstract: This survey presents an overview of information retrieval, natural languageprocessing and machine learning research that makes use of forumdata, including both discussion forums and community questionanswering(cQA) archives. The focus is on automated analysis, withthe goal of gaining a better understanding of the data and its users.We discuss the different strategies used for both retrieval tasks(post retrieval, question retrieval, and answer retrieval) and classificationtasks (post type classification, question classification, post qualityassessment, subjectivity, and viewpoint classification) at the postlevel, as well as at the thread level (thread retrieval, solvedness andtask orientation, discourse structure recovery and dialogue act tagging,QA-pair extraction, and thread summarisation). We also review workon forum users, including user satisfaction, expert finding, questionrecommendation and routing, and community analysis.The survey includes a brief history of forums, an overview of thedifferent kinds of forums, a summary of publicly available datasets forforum research, and a short discussion on the evaluation of retrievaltasks using forum data.The aim is to give a broad overview of the different kinds of forumresearch, a summary of the methods that have been applied, some insightsinto successful strategies, and potential areas for future research.Suggested CitationDoris Hoogeveen, Li Wang, Timothy Baldwin and Karin M. Verspoor (2017), "Web Forum Retrieval and Text Analytics: A Survey", Foundations and Trends® in Information Retrieval: Vol. 12: No. 1, pp 1-163. http://dx.doi.org/10.1561/1500000062
      PubDate: Tue, 31 Jan 2017 00:00:00 +010
       
  • A Survey of Query Auto Completion in Information Retrieval
    • Abstract: In information retrieval, query auto completion (QAC), also known as type-ahead [Xiao et al., 2013, Cai et al., 2014b] and auto-complete suggestion [Jain and Mishne, 2010], refers to the following functionality: given a prefix consisting of a number of characters entered into a search box, the user interface proposes alternative ways of extending the prefix to a full query. Ranking query completions is a challenging task due to the limited length of prefixes entered by users, the large volume of possible query completions matching a prefix, and the broad range of possible search intents. In recent years, a large number of query auto completion approaches have been proposed that produce ranked lists of alternative query completions by mining query logs.In this survey, we review work on query auto completion that has been published before 2016. We focus mainly on web search and provide a formal definition of the query auto completion problem. We describe two dominant families of approaches to the query auto completion problem, one based on heuristic models and the other based on learning to rank. We also identify dominant trends in published work on query auto completion, viz. the use of time-sensitive signals and the use of user-specific signals. We describe the datasets and metrics that are used to evaluate algorithms for query auto completion. We also devote a chapter to efficiency and a chapter to presentation and interaction aspects of query auto completion. We end by discussing related tasks as well as potential research directions to further the area.Suggested CitationFei Cai and Maarten de Rijke (2016), "A Survey of Query Auto Completion in Information Retrieval", Foundations and Trends® in Information Retrieval: Vol. 10: No. 4, pp 273-363. http://dx.doi.org/10.1561/1500000055
      PubDate: Mon, 19 Sep 2016 00:00:00 +020
       
  • Semantic Search on Text and Knowledge Bases
    • Abstract: This article provides a comprehensive overview of the broad area of semantic search on text and knowledge bases. In a nutshell, semantic search is “search with meaning”. This “meaning” can refer to various parts of the search process: understanding the query (instead of just finding matches of its components in the data), understanding the data (instead of just searching it for such matches), or representing knowledge in a way suitable for meaningful retrieval.Semantic search is studied in a variety of different communities with a variety of different views of the problem. In this survey, we classify this work according to two dimensions: the type of data (text, knowledge bases, combinations of these) and the kind of search (keyword, structured, natural language). We consider all nine combinations. The focus is on fundamental techniques, concrete systems, and benchmarks. The survey also considers advanced issues: ranking, indexing, ontology matching and merging, and inference. It also provides a succinct overview of fundamental natural language processing techniques: POS-tagging, named-entity recognition and disambiguation, sentence parsing, and distributional semantics.The survey is as self-contained as possible, and should thus also serve as a good tutorial for newcomers to this fascinating and highly topical field.Suggested CitationHannah Bast, Björn Buchhold and Elmar Haussmann (2016), "Semantic Search on Text and Knowledge Bases", Foundations and Trends® in Information Retrieval: Vol. 10: No. 2-3, pp 119-271. http://dx.doi.org/10.1561/1500000032
      PubDate: Wed, 22 Jun 2016 00:00:00 +020
       
  • Online Evaluation for Information Retrieval
    • Abstract: Online evaluation is one of the most common approaches to measure the effectiveness of an information retrieval system. It involves fielding the information retrieval system to real users, and observing these users’ interactions in-situ while they engage with the system. This allows actual users with real world information needs to play an important part in assessing retrieval quality. As such, online evaluation complements the common alternative offline evaluation approaches which may provide more easily interpretable outcomes, yet are often less realistic when measuring of quality and actual user experience.In this survey, we provide an overview of online evaluation techniques for information retrieval. We show how online evaluation is used for controlled experiments, segmenting them into experiment designs that allow absolute or relative quality assessments. Our presentation of different metrics further partitions online evaluation based on different sized experimental units commonly of interest: documents, lists and sessions. Additionally, we include an extensive discussion of recent work on data re-use, and experiment estimation based on historical data.A substantial part of this work focuses on practical issues: How to run evaluations in practice, how to select experimental parameters, how to take into account ethical considerations inherent in online evaluations, and limitations that experimenters should be aware of. While most published work on online experimentation today is at large scale in systems with millions of users, we also emphasize that the same techniques can be applied at small scale. To this end, we emphasize recent work that makes it easier to use at smaller scales and encourage studying real-world information seeking in a wide range of scenarios. Finally, we present a summary of the most recent work in the area, and describe open problems, as well as postulating future directions.Suggested CitationKatja Hofmann, Lihong Li and Filip Radlinski (2016), "Online Evaluation for Information Retrieval", Foundations and Trends® in Information Retrieval: Vol. 10: No. 1, pp 1-117. http://dx.doi.org/10.1561/1500000051
      PubDate: Wed, 22 Jun 2016 00:00:00 +020
       
  • Credibility in Information Retrieval
    • PubDate: 2015-12-22T23:20:50-05:00
      Issue No: Vol. 9, No. 5 (2015)
       
  • Information Retrieval with Verbose Queries
    • Abstract: Recently, the focus of many novel search applications has shifted from short keyword queries to verbose natural language queries. Examples include question answering systems and dialogue systems, voice search on mobile devices and entity search engines like Facebook’s Graph Search or Google’s Knowledge Graph. However the performance of textbook information retrieval techniques for such verbose queries is not as good as that for their shorter counterparts. Thus, effective handling of verbose queries has become a critical factor for adoption of information retrieval techniques in this new breed of search applications. Over the past decade, the information retrieval community has deeply explored the problem of transforming natural language verbose queries using operations like reduction, weighting, expansion, reformulation and segmentation into more effective structural representations. However, thus far, there was not a coherent and organized survey on this topic. In this survey, we aim to put together various research pieces of the puzzle, provide a comprehensive and structured overview of various proposed methods, and also list various application scenarios where effective verbose query processing can make a significant difference.Suggested CitationManish Gupta and Michael Bendersky (2015), "Information Retrieval with Verbose Queries", Foundations and Trends® in Information Retrieval: Vol. 9: No. 3-4, pp 209-354. http://dx.doi.org/10.1561/1500000050
      PubDate: Fri, 31 Jul 2015 00:00:00 +020
       
  • Temporal Information Retrieval
    • Abstract: Temporal dynamics and how they impact upon various components of information retrieval (IR) systems have received a large share of attention in the last decade. In particular, the study of relevance in information retrieval can now be framed within the so-called temporal IR approaches, which explain how user behavior, document content and scale vary with time, and how we can use them in our favor in order to improve retrieval effectiveness. This survey provides a comprehensive overview of temporal IR approaches, centered on the following questions: what are temporal dynamics, why do they occur, and when and how to leverage temporal information throughout the search cycle and architecture. We first explain the general and wide aspects associated to temporal dynamics by focusing on the web domain, from content and structural changes to variations of user behavior and interactions. Next, we pinpoint several research issues and the impact of such temporal characteristics on search, essentially regarding processing dynamic content, temporal query analysis and time-aware ranking. We also address particular aspects of temporal information extraction (for instance, how to timestamp documents and generate temporal profiles of text). To this end, we present existing temporal search engines and applications in related research areas, e.g., exploration, summarization, and clustering of search results, as well as future event retrieval and prediction, where the time dimension also plays an important role.Suggested CitationNattiya Kanhabua, Roi Blanco and Kjetil Nørvåg (2015), "Temporal Information Retrieval", Foundations and Trends® in Information Retrieval: Vol. 9: No. 2, pp 91-208. http://dx.doi.org/10.1561/1500000043
      PubDate: Thu, 09 Jul 2015 00:00:00 +020
       
  • Search Result Diversification
    • Abstract: Ranking in information retrieval has been traditionally approachedas a pursuit of relevant information, under the assumption that theusers’ information needs are unambiguously conveyed by their submittedqueries. Nevertheless, as an inherently limited representation of amore complex information need, every query can arguably be consideredambiguous to some extent. In order to tackle query ambiguity,search result diversification approaches have recently been proposed toproduce rankings aimed to satisfy the multiple possible informationneeds underlying a query. In this survey, we review the published literatureon search result diversification. In particular, we discuss themotivations for diversifying the search results for an ambiguous queryand provide a formal definition of the search result diversification problem.In addition, we describe the most successful approaches in theliterature for producing and evaluating diversity in multiple search domains.Finally, we also discuss recent advances as well as open researchdirections in the field of search result diversification.Suggested CitationRodrygo L. T. Santos, Craig Macdonald and Iadh Ounis (2015), "Search Result Diversification", Foundations and Trends® in Information Retrieval: Vol. 9: No. 1, pp 1-90. http://dx.doi.org/10.1561/1500000040
      PubDate: Thu, 05 Mar 2015 00:00:00 +010
       
 
 
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