Foundations and Trends in Information Retrieval最新文献

{"title":"Fairness in Information Access Systems","authors":"Michael D. Ekstrand, Anubrata Das, R. Burke, Fernando Diaz","doi":"10.1561/1500000079","DOIUrl":"https://doi.org/10.1561/1500000079","url":null,"abstract":"Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems. While fair information access shares many commonalities with fair classification, the multistakeholder nature of information access applications, the rank-based problem setting, the centrality of personalization in many cases, and the role of user response complicate the problem of identifying precisely what types and operationalizations of fairness may be relevant, let alone measuring or promoting them. In this monograph, we present a taxonomy of the various dimensions of fair information access and survey the literature to date on this new and rapidly-growing topic. We preface this with brief introductions to information access and algorithmic fairness, to facilitate use of this work by scholars with experience in one (or neither) of these fields who wish to learn about their intersection. We conclude with several open problems in fair information access, along with some suggestions for how to approach research in this space.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"1 1","pages":"1-177"},"PeriodicalIF":10.4,"publicationDate":"2021-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89800926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Search Interface Design and Evaluation","authors":"Chang Liu, Ying-Hsang Liu, Jingjing Liu, R. Bierig","doi":"10.1561/1500000073","DOIUrl":"https://doi.org/10.1561/1500000073","url":null,"abstract":"","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"77 1","pages":"243-416"},"PeriodicalIF":10.4,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87093182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extracting, Mining and Predicting Users' Interests from Social Media","authors":"F. Zarrinkalam, Stefano Faralli, Guangyuan Piao, E. Bagheri","doi":"10.1561/1500000078","DOIUrl":"https://doi.org/10.1561/1500000078","url":null,"abstract":"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 elicitation techniques that could have been considered to be intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users’ interests and preferences. In this monograph, we will cover five important subjects related to the mining of user interests from social media: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for Fattane Zarrinkalam, Stefano Faralli, Guangyuan Piao and Ebrahim Bagheri (2020), “Extracting, Mining and Predicting Users’ Interests from Social Media”, Foundations and Trends © in Information Retrieval: Vol. 14, No. 5, pp 445–617. DOI: 10.1561/1500000078. Full text available at: http://dx.doi.org/10.1561/1500000078","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"724 1","pages":"445-617"},"PeriodicalIF":10.4,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78742778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge Graphs: An Information Retrieval Perspective","authors":"Ridho Reinanda, E. Meij, M. de Rijke","doi":"10.1561/1500000063","DOIUrl":"https://doi.org/10.1561/1500000063","url":null,"abstract":"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 task-oriented organization of the material that we discuss. As an understanding of the intersection of IR and KGs is beneficial to many researchers and practitioners, we consider prior work from two complementary angles: leveraging KGs for information retrieval and enriching KGs using IR techniques. We start by discussing how KGs can be employed to support IR tasks, including document and entity retrieval. We then proceed by describing how IR—and language technology in general—can be utilized for the construction and completion of KGs. This includes tasks such as entity recognition, typing, and relation extraction. We discuss common issues that appear across the tasks that we consider and identify future directions for addressing them. We also provide pointers to datasets and other resources that should be useful for both newcomers and experienced researchers in the area. Ridho Reinanda, Edgar Meij and Maarten de Rijke (2020), “Knowledge Graphs: An Information Retrieval Perspective”, Foundations and Trends® in Information Retrieval: Vol. 14, No. 4, pp 289–444. DOI: 10.1561/1500000063. Full text available at: http://dx.doi.org/10.1561/1500000063","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"3 1","pages":"289-444"},"PeriodicalIF":10.4,"publicationDate":"2020-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77053977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning for Matching in Search and Recommendation","authors":"Jun Xu, Xiangnan He, Hang Li","doi":"10.1561/1500000076","DOIUrl":"https://doi.org/10.1561/1500000076","url":null,"abstract":"<p>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 learning techniques for matching tasks in search and recommendation. With the availability of a large amount of data, powerful computational resources, and advanced deep learning techniques, deep learning for matching now becomes the state-of-the-art technology for search and recommendation. The key to the success of the deep learning approach is its strong ability in learning of representations and generalization of matching patterns from data (e.g., queries, documents, users, items, and contexts, particularly in their raw forms).<p>This survey gives a systematic and comprehensive introduction to the deep matching models for search and recommendation developed recently. It first gives a unified view of matching in search and recommendation. In this way, the solutions from the two fields can be compared under one framework. Then, the survey categorizes the current deep learning solutions into two types: methods of representation learning and methods of matching function learning. The fundamental problems, as well as the state-of-the-art solutions of query-document matching in search and user-item matching in recommendation, are described. The survey aims to help researchers from both search and recommendation communities to get in-depth understanding and insight into the spaces, stimulate more ideas and discussions, and promote developments of new technologies.</p><p>Matching is not limited to search and recommendation. Similar problems can be found in paraphrasing, question answering, image annotation, and many other applications. In general, the technologies introduced in the survey can be generalized into a more general task of matching between objects from two spaces.</p></p>","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"12 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138542972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information Retrieval: The Early Years","authors":"D. Harman","doi":"10.1561/1500000065","DOIUrl":"https://doi.org/10.1561/1500000065","url":null,"abstract":"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 ideas were limited by lack of computer power, and additionally by lack of machine-readable text. But each decade saw progress, and by the 1990s, it had flowered. This monograph tells the story of the early history of information retrieval (up until 2000) in a manner that presents the technical context, the research and the early commercialization efforts. Donna Harman (2019), “Information Retrieval: The Early Years”, Foundations and Trends © in Information Retrieval: Vol. 13, No. 5, pp 425–577. DOI: 10.1561/1500000065. Full text available at: http://dx.doi.org/10.1561/1500000065","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"114 1","pages":"425-577"},"PeriodicalIF":10.4,"publicationDate":"2019-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87980781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bandit Algorithms in Information Retrieval","authors":"D. Glowacka","doi":"10.1561/1500000067","DOIUrl":"https://doi.org/10.1561/1500000067","url":null,"abstract":"","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"95 1","pages":"299-424"},"PeriodicalIF":10.4,"publicationDate":"2019-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85376119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Approaches to Conversational AI","authors":"Jianfeng Gao, Michel Galley, Lihong Li","doi":"10.1561/1500000074","DOIUrl":"https://doi.org/10.1561/1500000074","url":null,"abstract":"<p>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 the progress that has been made and challenges still being faced, using specific systems and models as case studies.\u0000</p>","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"54 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2019-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138526301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Query Processing for Scalable Web Search","authors":"N. Tonellotto, C. Macdonald, I. Ounis","doi":"10.1561/1500000057","DOIUrl":"https://doi.org/10.1561/1500000057","url":null,"abstract":"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 engine can also make it less efficient. Meanwhile, search engines continue to rapidly evolve, with larger indexes, more complex retrieval strategies and growing query volumes. Hence, there is a need for the development of efficient query processing infrastructures that make appropriate sacrifices in effectiveness in order to make gains in efficiency. This survey comprehensively reviews the foundations of search engines, from index layouts to basic term-at-a-time (TAAT) and document-at-a-time (DAAT) query processing strategies, while also providing the latest trends in the literature in efficient query processing, including the coherent and systematic reviews of techniques such as dynamic pruning and impact-sorted posting lists as well as their variants and optimisations. Our explanations of query processing strategies, for instance the WAND and BMW dynamic pruning algorithms, are presented with illustrative figures showing how the processing state changes as the algorithms progress. Moreover, acknowledging the recent trends in applying a cascading infrastructure within search systems, this survey describes techniques for efficiently integrating effective learned models, such as those obtained from learning-torank techniques. The survey also covers the selective application of query processing techniques, often achieved by predicting the response times of the search engine (known as query efficiency prediction), and making per-query tradeoffs between efficiency and effectiveness to ensure that the required retrieval speed targets can be met. Finally, the survey concludes with a summary of open directions in efficient search infrastructures, namely the use of signatures, real-time, energy-efficient and modern hardware & software architectures.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"40 1","pages":"319-500"},"PeriodicalIF":10.4,"publicationDate":"2018-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84551326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Introduction to Neural Information Retrieval","authors":"Bhaskar Mitra, Nick Craswell","doi":"10.1561/1500000061","DOIUrl":"https://doi.org/10.1561/1500000061","url":null,"abstract":"Neural models have been employed in many Information Retrieval scenarios, including ad-hoc retrieval, recommender systems, multi-media search, and even conversational systems that generate answers in response to natural language questions. An Introduction to Neural Information Retrieval provides a tutorial introduction to neural methods for ranking documents in response to a query, an important IR task. The monograph provides a complete picture of neural information retrieval techniques that culminate in supervised neural learning to rank models including deep neural network architectures that are trained end-to-end for ranking tasks. In reaching this point, the authors cover all the important topics, including the learning to rank framework and an overview of deep neural networks. This monograph provides an accessible, yet comprehensive, overview of the state-of-the-art of Neural Information Retrieval.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"32 1","pages":"1-126"},"PeriodicalIF":10.4,"publicationDate":"2018-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86139477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}