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  • Extracting, Mining and Predicting Users’ Interests from Social Media
    Found. Trends Inf. Ret. (IF 5.143) Pub Date : 2020-11-4
    Fattane Zarrinkalam; Stefano Faralli; Guangyuan Piao; Ebrahim Bagheri

    The abundance of user generated content on social media provides the opportunity to build models that are able to accurately and effectively extract, mine and predict users’ interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference

    更新日期:2020-11-06
  • 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
Contents have been reproduced by permission of the publishers.
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