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  • Deep Learning for Matching in Search and Recommendation
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2020-7-13
    Jun Xu; Xiangnan He; Hang Li

    Matching is a key problem in both search and recommendation, which is to measure the relevance of a document to a query or the interest of a user to an item. Machine learning has been exploited to address the problem, which learns a matching function based on input representations and from labeled data, also referred to as “learning to match”. In recent years, efforts have been made to develop deep

    更新日期:2020-08-20
  • Knowledge Graphs: An Information Retrieval Perspective
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2020-10-30
    Ridho Reinanda; Edgar Meij; Maarten de Rijke

    In this survey, we provide an overview of the literature on knowledge graphs (KGs) in the context of information retrieval (IR). Modern IR systems can benefit from information available in KGs in multiple ways, independent of whether the KGs are publicly available or proprietary ones. We provide an overview of the components required when building IR systems that leverage KGs and use a taskoriented

    更新日期:2020-08-20
  • Explainable Recommendation: A Survey and New Perspectives
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2020-3-10
    Yongfeng Zhang; Xu Chen

    Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model in some contexts). Explainable recommendation tries to address the problem of why: by providing explanations to users or system designers

    更新日期:2020-03-10
  • Information Retrieval: The Early Years
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2019-7-8
    Donna Harman

    Information retrieval, the science behind search engines, had its birth in the late 1950s. Its forbearers came from library science, mathematics and linguistics, with later input from computer science. The early work dealt with finding better ways to index text, and then using new algorithms to search these (mostly) automatically built indexes. Like all computer applications, however, the theory and

    更新日期:2019-07-08
  • Bandit Algorithms in Information Retrieval
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2019-5-22
    Dorota Glowacka

    Bandit algorithms, named after casino slot machines sometimes known as “one-armed bandits”, fall into a broad category of stochastic scheduling problems. In the setting with multiple arms, each arm generates a reward with a given probability. The gambler’s aim is to find the arm producing the highest payoff and then continue playing in order to accumulate the maximum reward possible. However, having

    更新日期:2019-05-22
  • Neural Approaches to Conversational AI
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2019-2-20
    Jianfeng Gao; Michel Galley; Lihong Li

    The present paper surveys neural approaches to conversational AI that have been developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) chatbots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between them and traditional approaches, and discuss

    更新日期:2019-02-20
  • An Introduction to Neural Information Retrieval
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2018-12-22
    Bhaskar Mitra; Nick Craswell

    Neural ranking models for information retrieval (IR) use shallow or deep neural networks to rank search results in response to a query. Traditional learning to rank models employ supervised machine learning (ML) techniques—including neural networks—over hand-crafted IR features. By contrast, more recently proposed neural models learn representations of language from raw text that can bridge the gap

    更新日期:2018-12-22
  • Efficient Query Processing for Scalable Web Search
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2018-12-22
    Nicola Tonellotto; Craig Macdonald; Iadh Ounis

    Search engines are exceptionally important tools for accessing information in today’s world. In satisfying the information needs of millions of users, the effectiveness (the quality of the search results) and the efficiency (the speed at which the results are returned to the users) of a search engine are two goals that form a natural trade-off, as techniques that improve the effectiveness of the search

    更新日期:2018-12-22
  • Geographic Information Retrieval: Progress and Challenges in Spatial Search of Text
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2018-2-20
    Ross S. Purves; Paul Clough; Christopher B. Jones; Mark H. Hall; Vanessa Murdock

    Significant amounts of information available today contain references to places on earth. Traditionally such information has been held as structured data and was the concern of Geographic Information Systems (GIS). However, increasing amounts of data in the form of unstructured text are available for indexing and retrieval that also contain spatial references. This monograph describes the field of

    更新日期:2018-02-20
  • Web Forum Retrieval and Text Analytics: A Survey
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2018-1-2
    Doris Hoogeveen; Li Wang; Timothy Baldwin; Karin M. Verspoor

    This survey presents an overview of information retrieval, natural language processing and machine learning research that makes use of forum data, including both discussion forums and community questionanswering (cQA) archives. The focus is on automated analysis, with the goal of gaining a better understanding of the data and its users. We discuss the different strategies used for both retrieval tasks

    更新日期:2018-01-02
  • Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2017-7-23
    Jun Wang; Weinan Zhang; Shuai Yuan

    The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers

    更新日期:2017-07-23
  • Applications of Topic Models
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2017-7-19
    Jordan Boyd-Graber; Yuening Hu; David Mimno

    How can a single person understand what’s going on in a collection of millions of documents? This is an increasingly common problem: sifting through an organization’s e-mails, understanding a decade worth of newspapers, or characterizing a scientific field’s research. Topic models are a statistical framework that help users understand large document collections: not just to find individual documents

    更新日期:2017-07-19
  • Searching the Enterprise
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2017-7-11
    Udo Kruschwitz; Charlie Hull

    Search has become ubiquitous but that does not mean that search has been solved. Enterprise search, which is broadly speaking the use of information retrieval technology to find information within organisations, is a good example to illustrate this. It is an area that is of huge importance for businesses, yet has attracted relatively little academic interest. This monograph will explore the main issues

    更新日期:2017-07-11
  • Aggregated Search
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2017-3-5
    Jaime Arguello

    The goal of aggregated search is to provide integrated search across multiple heterogeneous search services in a unified interface—a single query box and a common presentation of results. In the web search domain, aggregated search systems are responsible for integrating results from specialized search services, or verticals, alongside the core web results. For example, search portals such as Google

    更新日期:2017-03-05
  • A Survey of Query Auto Completion in Information Retrieval
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2016-9-18
    Fei Cai; Maarten de Rijke

    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

    更新日期:2016-09-18
  • Online Evaluation for Information Retrieval
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2016-6-21
    Katja Hofmann; Lihong Li; Filip Radlinski

    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

    更新日期:2016-06-21
  • Semantic Search on Text and Knowledge Bases
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2016-6-21
    Hannah Bast; Björn Buchhold; Elmar Haussmann

    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

    更新日期:2016-06-21
  • Credibility in Information Retrieval
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2015-12-17
    Alexandru L. Ginsca; Adrian Popescu; Mihai Lupu

    Credibility, as the general concept covering trustworthiness and expertise, but also quality and reliability, is strongly debated in philosophy, psychology, and sociology, and its adoption in computer science is therefore fraught with difficulties. Yet its importance has grown in the information access community because of two complementing factors: on one hand, it is relatively difficult to precisely

    更新日期:2015-12-17
  • Information Retrieval with Verbose Queries
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2015-7-30
    Manish Gupta; Michael Bendersky

    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

    更新日期:2015-07-30
  • Temporal Information Retrieval
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2015-7-8
    Nattiya Kanhabua; Roi Blanco; Kjetil Nørvåg

    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

    更新日期:2015-07-08
  • Search Result Diversification
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2015-3-4
    Rodrygo L. T. Santos; Craig Macdonald; Iadh Ounis

    Ranking in information retrieval has been traditionally approached as a pursuit of relevant information, under the assumption that the users’ information needs are unambiguously conveyed by their submitted queries. Nevertheless, as an inherently limited representation of a more complex information need, every query can arguably be considered ambiguous to some extent. In order to tackle query ambiguity

    更新日期:2015-03-04
  • Computational Advertising: Techniques for Targeting Relevant Ads
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2014-10-28
    Kushal Dave; Vasudeva Varma

    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

    更新日期:2014-10-28
  • Music Information Retrieval: Recent Developments and Applications
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2014-9-11
    Markus Schedl; Emilia Gómez; Julián Urbano

    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

    更新日期:2014-09-11
  • LifeLogging: Personal Big Data
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2014-6-15
    Cathal Gurrin; Alan F. Smeaton; Aiden R. Doherty

    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

    更新日期:2014-06-15
  • Semantic Matching in Search
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2014-6-11
    Hang Li; Jun Xu

    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

    更新日期:2014-06-11
  • Arabic Information Retrieval
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2014-2-4
    Kareem Darwish; Walid Magdy

    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

    更新日期:2014-02-04
  • Information Retrieval for E-Discovery
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2013-6-25
    Douglas W. Oard; William Webber

    E-discovery refers generally to the process by which one party (for example, the plaintiff) is entitled to "discover" evidence in the form of "electronically stored information" that is held by another party (for example, the defendant), and that is relevant to some matter that is the subject of civil litigation (that is, what is commonly called a "lawsuit"). This survey describes the emergence of

    更新日期:2013-06-25
  • Patent Retrieval
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2013-2-19
    Mihai Lupu; Allan Hanbury

    Intellectual property and the patent system in particular have been extremely present in research and discussion, even in the public media, in the last few years. Without going into any controversial issues regarding the patent system, we approach a very real and growing problem: searching for innovation. The target collection for this task does not consist of patent documents only, but it is in these

    更新日期:2013-02-19
  • Contextual Search: A Computational Framework
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2012-12-4
    Massimo Melucci

    The growing availability of data in electronic form, the expansion of the World Wide Web (WWW) and the accessibility of computational methods for large-scale data processing have allowed researchers in Information Retrieval (IR) to design systems which can effectively and efficiently constrain search within the boundaries given by context, thus transforming classical search into contextual search.

    更新日期:2012-12-04
  • Expertise Retrieval
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2012-7-29
    Krisztian Balog; Yi Fang; Maarten de Rijke; Pavel Serdyukov; Luo Si

    People have looked for experts since before the advent of computers. With advances in information retrieval technology and the large-scale availability of digital traces of knowledge-related activities, computer systems that can fully automate the process of locating expertise have become a reality. The past decade has witnessed tremendous interest, and a wealth of results, in expertise retrieval as

    更新日期:2012-07-29
  • Information Retrieval on the Blogosphere
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2012-7-29
    Rodrygo L. T. Santos; Craig Macdonald; Richard McCreadie; Iadh Ounis; Ian Soboroff

    Blogs have recently emerged as a new open, rapidly evolving and reactive publishing medium on the Web. Rather than managed by a central entity, the content on the blogosphere — the collection of all blogs on the Web — is produced by millions of independent bloggers, who can write about virtually anything. This open publishing paradigm has led to a growing mass of user-generated content on theWeb, which

    更新日期:2012-07-29
  • Spoken Content Retrieval: A Survey of Techniques and Technologies
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2012-7-22
    Martha Larson; Gareth J. F. Jones

    Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing

    更新日期:2012-07-22
  • Automatic Summarization
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2011-6-29
    Ani Nenkova; Kathleen McKeown

    It has now been 50 years since the publication of Luhn's seminal paper on automatic summarization. During these years the practical need for automatic summarization has become increasingly urgent and numerous papers have been published on the topic. As a result, it has become harder to find a single reference that gives an overview of past efforts or a complete view of summarization tasks and necessary

    更新日期:2011-06-29
  • Federated Search
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2011-3-6
    Milad Shokouhi; Luo Si

    Federated search (federated information retrieval or distributed information retrieval) is a technique for searching multiple text collections simultaneously. Queries are submitted to a subset of collections that are most likely to return relevant answers. The results returned by selected collections are integrated and merged into a single list. Federated search is preferred over centralized search

    更新日期:2011-03-06
  • Adversarial Web Search
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2011-1-21
    Carlos Castillo; Brian D. Davison

    Web search engines have become indispensable tools for finding content. As the popularity of the Web has increased, the efforts to exploit the Web for commercial, social, or political advantage have grown, making it harder for search engines to discriminate between truthful signals of content quality and deceptive attempts to game search engines' rankings. This problem is further complicated by the

    更新日期:2011-01-21
  • Test Collection Based Evaluation of Information Retrieval Systems
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2010-6-21
    Mark Sanderson

    Use of test collections and evaluation measures to assess the effectiveness of information retrieval systems has its origins in work dating back to the early 1950s. Across the nearly 60 years since that work started, use of test collections is a de facto standard of evaluation. This monograph surveys the research conducted and explains the methods and measures devised for evaluation of retrieval systems

    更新日期:2010-06-21
  • Web Crawling
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2010-2-11
    Christopher Olston; Marc Najork

    This is a survey of the science and practice of web crawling. While at first glance web crawling may appear to be merely an application of breadth-first-search, the truth is that there are many challenges ranging from systems concerns such as managing very large data structures to theoretical questions such as how often to revisit evolving content sources. This survey outlines the fundamental challenges

    更新日期:2010-02-11
  • The Probabilistic Relevance Framework: BM25 and Beyond
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2009-12-16
    Stephen Robertson; Hugo Zaragoza

    The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970–1980s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account document meta-data (especially structure and link-graph information). Again,

    更新日期:2009-12-16
  • Mining Query Logs: Turning Search Usage Data into Knowledge
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2009-11-28
    Fabrizio Silvestri

    Web search engines have stored in their logs information about users since they started to operate. This information often serves many purposes. The primary focus of this survey is on introducing to the discipline of query mining by showing its foundations and by analyzing the basic algorithms and techniques that are used to extract useful knowledge from this (potentially) infinite source of information

    更新日期:2009-11-28
  • Learning to Rank for Information Retrieval
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2009-6-26
    Tie-Yan Liu

    Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking model using training data, such that the model can sort new objects according to their degrees of relevance, preference, or importance. Many IR problems are by nature ranking problems, and many IR technologies can be potentially enhanced by using learning-to-rank techniques. The objective of this tutorial

    更新日期:2009-06-26
  • Concept-Based Video Retrieval
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2009-5-26
    Cees G. M. Snoek; Marcel Worring

    In this paper, we review 300 references on video retrieval, indicating when text-only solutions are unsatisfactory and showing the promising alternatives which are in majority concept-based. Therefore, central to our discussion is the notion of a semantic concept: an objective linguistic description of an observable entity. Specifically, we present our view on how its automated detection, selection

    更新日期:2009-05-26
  • Methods for Evaluating Interactive Information Retrieval Systems with Users
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2009-4-27
    Diane Kelly

    This paper provides overview and instruction regarding the evaluation of interactive information retrieval systems with users. The primary goal of this article is to catalog and compile material related to this topic into a single source. This article (1) provides historical background on the development of user-centered approaches to the evaluation of interactive information retrieval systems; (2)

    更新日期:2009-04-27
  • Statistical Language Models for Information Retrieval A Critical Review
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2008-11-29
    ChengXiang Zhai

    Statistical language models have recently been successfully applied to many information retrieval problems. A great deal of recent work has shown that statistical language models not only lead to superior empirical performance, but also facilitate parameter tuning and open up possibilities for modeling nontraditional retrieval problems. In general, statistical language models provide a principled way

    更新日期:2008-11-29
  • Opinion Mining and Sentiment Analysis
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2008-7-6
    Bo Pang; Lillian Lee

    An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption

    更新日期:2008-07-06
  • Email Spam Filtering: A Systematic Review
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2008-6-22
    Gordon V. Cormack

    Spam is information crafted to be delivered to a large number of recipients, in spite of their wishes. A spam filter is an automated tool to recognize spam so as to prevent its delivery. The purposes of spam and spam filters are diametrically opposed: spam is effective if it evades filters, while a filter is effective if it recognizes spam. The circular nature of these definitions, along with their

    更新日期:2008-06-22
  • Authorship Attribution
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2008-3-6
    Patrick Juola

    Authorship attribution, the science of inferring characteristics of the author from the characteristics of documents written by that author, is a problem with a long history and a wide range of application. Recent work in "non-traditional" authorship attribution demonstrates the practicality of automatically analyzing documents based on authorial style, but the state of the art is confusing. Analyses

    更新日期:2008-03-06
  • Open-Domain Question–Answering
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2007-8-20
    John Prager

    The increasing availability of music in digital format needs to be matched by the development of tools for music accessing, filtering, classification, and retrieval. The research area of Music Information Retrieval (MIR) covers many of these aspects. The aim of this paper is to present an overview of this vast and new field. A number of issues, which are peculiar to the music language, are described–including

    更新日期:2007-08-20
  • Music Retrieval: A Tutorial and Review
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2006-10-25
    Nicola Orio

    The increasing availability of music in digital format needs to be matched by the development of tools for music accessing, filtering, classification, and retrieval. The research area of Music Information Retrieval (MIR) covers many of these aspects. The aim of this paper is to present an overview of this vast and new field. A number of issues, which are peculiar to the music language, are described–including

    更新日期:2006-10-25
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