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Journal Cover Foundations and Trends® in Information Retrieval
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   ISSN (Print) 1554-0669 - ISSN (Online) 1554-0677
   Published by Now Publishers Inc Homepage  [30 journals]
  • 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.
      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.
      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.
      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.
      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.
      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.
      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.
      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.
      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.
      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.
      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.
      PubDate: Thu, 05 Mar 2015 00:00:00 +010
  • Computational Advertising: Techniques for Targeting Relevant Ads
    • Abstract: Computational Advertising, popularly known as online advertising or Web advertising, refers to finding the most relevant ads matching a particular context on the Web. The context depends on the type of advertising and could mean – content where the ad is shown, the user who is viewing the ad or the social network of the user. Computational Advertising (CA) is a scientific sub-discipline at the intersection of information retrieval, statistical modeling, machine learning, optimization, large scale search and text analysis. The core problem addressed in Computational Advertising is of match-making between the ads and the context.CA is prevalent in three major forms on the Web. One of the forms involves showing textual ads relevant to a query on the search page, known as Sponsored Search. On the other hand, showing textual ads relevant to a third party webpage content is known as Contextual Advertising. The third form of advertising also deals with the placement of ads on third party Web pages, but the ads in this form are rich multimedia ads – image, video, audio, flash. The business model with rich media ads is slightly different from the ones with textual ads. These ads are also called banner ads, and this form of advertising is known as Display Advertising.Both Sponsored Search and Contextual Advertising involve retrieving relevant ads for different types of content (query and Web page). As ads are short and are mainly written to attract the user, retrieval of ads pose challenges like vocabulary mismatch between the query/content and the ad. Also, as the user’s probability of examining an ad decreases with the position of the ad in the ranked list, it is imperative to keep the best ads at the top positions. Display Advertising poses several challenges including modeling user behaviour and noisy page content and bid optimization on the advertiser’s side. Additionally, online advertising faces challenges like false bidding, click spam and ad spam. These challenges are prevalent in all forms of advertising. There has been a lot of research work published in different areas of CA in the last one and a half decade. The focus of this survey is to discuss the problems and solutions pertaining to the information retrieval, machine learning and statistics domain of CA. This survey covers techniques and approaches that deal with several issues mentioned above.Research in Computational Advertising has evolved over time and currently continues both in traditional areas (vocabulary mismatch, query rewriting, click prediction) and recently identified areas (user targeting, mobile advertising, social advertising). In this study, we predominantly focus on the problems and solutions proposed in traditional areas in detail and briefly cover the emerging areas in the latter half of the survey. To facilitate future research, a discussion of available resources, list of public benchmark datasets and future directions of work is also provided in the end.Suggested CitationKushal Dave and Vasudeva Varma (2014), "Computational Advertising: Techniques for Targeting Relevant Ads", Foundations and Trends® in Information Retrieval: Vol. 8: No. 4–5, pp 263-418.
      PubDate: Wed, 29 Oct 2014 00:00:00 +010
  • Music Information Retrieval: Recent Developments and Applications
    • Abstract: We provide a survey of the field of Music Information Retrieval (MIR), in particular paying attention to latest developments, such as semantic auto-tagging and user-centric retrieval and recommendation approaches. We first elaborate on well-established and proven methods for feature extraction and music indexing, from both the audio signal and contextual data sources about music items, such as web pages or collaborative tags. These in turn enable a wide variety of music retrieval tasks, such as semantic music search or music identification (“query by example"). Subsequently, we review current work on user analysis and modeling in the context of music recommendation and retrieval, addressing the recent trend towards user-centric and adaptive approaches and systems. A discussion follows about the important aspect of how various MIR approaches to different problems are evaluated and compared. Eventually, a discussion about the major open challenges concludes the survey.Suggested CitationMarkus Schedl, Emilia Gómez and Julián Urbano (2014), "Music Information Retrieval: Recent Developments and Applications", Foundations and Trends® in Information Retrieval: Vol. 8: No. 2-3, pp 127-261.
      PubDate: Fri, 12 Sep 2014 00:00:00 +020
  • LifeLogging: Personal Big Data
    • Abstract: We have recently observed a convergence of technologies to foster the emergence of lifelogging as a mainstream activity. Computer storage has become significantly cheaper, and advancements in sensing technology allows for the efficient sensing of personal activities, locations and the environment. This is best seen in the growing popularity of the quantified self movement, in which life activities are tracked using wearable sensors in the hope of better understanding human performance in a variety of tasks. This review aims to provide a comprehensive summary of lifelogging, to cover its research history, current technologies, and applications. Thus far, most of the lifelogging research has focused predominantly on visual lifelogging, hence we maintain this focus in this review. However, we also reflect on the challenges lifelogging poses for information access and retrieval in general. This review is a suitable reference for those seeking an information retrieval scientist’s perspective on lifelogging and the quantified self.Suggested CitationCathal Gurrin, Alan F. Smeaton and Aiden R. Doherty (2014), "LifeLogging: Personal Big Data", Foundations and Trends® in Information Retrieval: Vol. 8: No. 1, pp 1-125.
      PubDate: Mon, 16 Jun 2014 00:00:00 +020
  • Semantic Matching in Search
    • Abstract: Relevance is the most important factor to assure users’ satisfaction in search and the success of a search engine heavily depends on its performance on relevance. It has been observed that most of the dissatisfaction cases in relevance are due to term mismatch between queries and documents (e.g., query “NY times” does not match well with a document only containing “New York Times”), because term matching, i.e., the bag-of-words approach, still functions as the main mechanism of modern search engines. It is not exaggerated to say, therefore, that mismatch between query and document poses the most critical challenge in search. Ideally, one would like to see query and document match with each other, if they are topically relevant. Recently, researchers have expended significant effort to address the problem. The major approach is to conduct semantic matching, i.e., to perform more query and document understanding to represent the meanings of them, and perform better matching between the enriched query and document representations. With the availability of large amounts of log data and advanced machine learning techniques, this becomes more feasible and significant progress has been made recently. This survey gives a systematic and detailed introduction to newly developed machine learning technologies for query document matching (semantic matching) in search, particularly web search. It focuses on the fundamental problems, as well as the state-of-the-art solutions of query document matching on form aspect, phrase aspect, word sense aspect, topic aspect, and structure aspect. The ideas and solutions explained may motivate industrial practitioners to turn the research results into products. The methods introduced and the discussions made may also stimulate academic researchers to find new research directions and approaches. Matching between query and document is not limited to search and similar problems can be found in question answering, online advertising, cross-language information retrieval, machine translation, recommender systems, link prediction, image annotation, drug design, and other applications, as the general task of matching between objects from two different spaces. The technologies introduced can be generalized into more general machine learning techniques, which is referred to as learning to match in this survey.Suggested CitationHang Li and Jun Xu (2014), "Semantic Matching in Search", Foundations and Trends® in Information Retrieval: Vol. 7: No. 5, pp 343-469.
      PubDate: Thu, 12 Jun 2014 00:00:00 +020
  • Arabic Information Retrieval
    • Abstract: In the past several years, Arabic Information Retrieval (IR) has garnered significant attention. The main research interests have focused on retrieval of formal language, mostly in the news domain, with ad hoc retrieval, OCR document retrieval, and cross-language retrieval. The literature on other aspects of retrieval continues to be sparse or non-existent, though some of these aspects have been investigated by industry. Others aspects of Arabic retrieval that have received attention include document image retrieval, speech search, social media and web search, and filtering. However, efforts on different aspects of Arabic retrieval continue to be deficient and severely lacking behind efforts in other languages. The survey covers: 1) general properties of the Arabic language; 2) some of the aspects of Arabic that affect retrieval; 3) Arabic processing necessary for effective Arabic retrieval; 4) Arabic retrieval in public IR evaluations; 5) specialized retrieval problems, namely Arabic-English CLIR, Arabic Document Image Retrieval, Arabic Social Search, Arabic Web Search, Question Answering, Image retrieval, and Arabic Speech Search; 6) Arabic IR and NLP resources; and 7) open IR problems that require further attention.Suggested CitationKareem Darwish and Walid Magdy (2014), "Arabic Information Retrieval", Foundations and Trends® in Information Retrieval: Vol. 7: No. 4, pp 239-342.
      PubDate: Wed, 05 Feb 2014 00:00:00 +010
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