Foundations and Trends in Information Retrieval最新文献

Conversational Information Seeking 会话信息搜索

IF 10.4 2区计算机科学

Foundations and Trends in Information Retrieval Pub Date : 2023-08-02 DOI: 10.1561/1500000081

Hamed Zamani, Johanne R. Trippas, Jeff Dalton, Filip Radlinski

引用次数: 49

Perspectives of Neurodiverse Participants in Interactive Information Retrieval 交互信息检索中神经多样性参与者的观点

IF 10.4 2区计算机科学

Foundations and Trends in Information Retrieval Pub Date : 2023-07-26 DOI: 10.1561/1500000086

Laurianne Sitbon, Gerd Berget, Margot Brereton

引用次数: 0

Efficient and Effective Tree-based and Neural Learning to Rank 高效的基于树和神经学习的排名

IF 10.4 2区计算机科学

Foundations and Trends in Information Retrieval Pub Date : 2023-05-14 DOI: 10.1561/1500000071

Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini

{"title":"Efficient and Effective Tree-based and Neural Learning to Rank","authors":"Sebastian Bruch, Claudio Lucchese, Franco Maria Nardini","doi":"10.1561/1500000071","DOIUrl":"https://doi.org/10.1561/1500000071","url":null,"abstract":"As information retrieval researchers, we not only develop algorithmic solutions to hard problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on the fundamental topic of retrieval and ranking, for instance, has a rich history of studying the effectiveness of indexes, retrieval algorithms, and complex machine learning rankers, while at the same time quantifying their computational costs, from creation and training to application and inference. This is evidenced, for example, by more than a decade of research on efficient training and inference of large decision forest models in Learning to Rank (LtR). As we move towards even more complex, deep learning models in a wide range of applications, questions on efficiency have once again resurfaced with renewed urgency. Indeed, efficiency is no longer limited to time and space; instead it has found new, challenging dimensions that stretch to resource-, sample- and energy-efficiency with ramifications for researchers, users, and the environment.This monograph takes a step towards promoting the study of efficiency in the era of neural information retrieval by offering a comprehensive survey of the literature on efficiency and effectiveness in ranking, and to a limited extent, retrieval. This monograph was inspired by the parallels that exist between the challenges in neural network-based ranking solutions and their predecessors, decision forest-based LtR models, as well as the connections between the solutions the literature to date has to offer. We believe that by understanding the fundamentals underpinning these algorithmic and data structure solutions for containing the contentious relationship between efficiency and effectiveness, one can better identify future directions and more efficiently determine the merits of ideas. We also present what we believe to be important research directions in the forefront of efficiency and effectiveness in retrieval and ranking.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"72 4","pages":""},"PeriodicalIF":10.4,"publicationDate":"2023-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49697987","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}

引用次数: 2

Quantum-Inspired Neural Language Representation, Matching and Understanding 量子启发的神经语言表示、匹配和理解

IF 10.4 2区计算机科学

Foundations and Trends in Information Retrieval Pub Date : 2023-04-18 DOI: 10.1561/1500000091

Peng Zhang, Hui Gao, Jing Zhang, Dawei Song

{"title":"Quantum-Inspired Neural Language Representation, Matching and Understanding","authors":"Peng Zhang, Hui Gao, Jing Zhang, Dawei Song","doi":"10.1561/1500000091","DOIUrl":"https://doi.org/10.1561/1500000091","url":null,"abstract":"The introduction of Quantum Theory (QT) provides a unified mathematical framework for Information Retrieval (IR). Compared with the classical IR framework, the quantuminspired IR framework is based on user-centered modeling methods to model non-classical cognitive phenomena in human relevance judgment in the IR process. With the increase of data and computing resources, neural IR methods have been applied to the text matching and understanding task of IR. Neural networks have a strong learning ability of effective representation and generalization of matching patterns from raw data. However, these methods show some unavoidable defects, such as the inability to model user cognitive phenomena, large number of model parameters and the “black box” characteristics of network structure. These problems greatly limit the development of neural IR and related fields. Although the quantum-inspired retrieval framework can theoretically solve the above problems, it is faced with problems such as poor model efficiency and difficulty in integrating with neural network, which lead to a huge gap between QT and neural network modeling.This review gives a systematic introduction to quantuminspired neural IR, including quantum-inspired neural language representation, matching and understanding. This is not only helpful to non-classical phenomena modeling in IR but also to break the theoretical bottleneck of neural networks and design more transparent neural IR models. We introduce the language representation method based on QT and the quantum-inspired text matching and decision making model under neural network, which shows its theoretical advantages in document ranking, relevance matching, multimodal IR, and can be integrated with neural networks to jointly promote the development of IR. The latest progress of quantum language understanding is introduced and further topics on QT and language modeling provide readers with more materials for thinking.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"54 45","pages":""},"PeriodicalIF":10.4,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49698420","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}

引用次数: 1

Deep Learning for Dialogue Systems: Chit-Chat and Beyond 对话系统的深度学习:闲聊和超越

IF 10.4 2区计算机科学

Foundations and Trends in Information Retrieval Pub Date : 2022-01-01 DOI: 10.1561/1500000083

Rui Yan, Juntao Li, Zhou Yu

引用次数: 10

Pre-training Methods in Information Retrieval 信息检索中的预训练方法

IF 10.4 2区计算机科学

Foundations and Trends in Information Retrieval Pub Date : 2022-01-01 DOI: 10.1561/1500000100

Yixing Fan, Xiaohui Xie, Yinqiong Cai, Jia Chen, Xinyv Ma, Xiangsheng Li, Ruqing Zhang, Jiafeng Guo

引用次数: 5

Psychology-informed Recommender Systems 基于心理学的推荐系统

IF 10.4 2区计算机科学

Foundations and Trends in Information Retrieval Pub Date : 2021-12-06 DOI: 10.1561/1500000090

E. Lex, Dominik Kowald, Paul Seitlinger, Thi Ngoc Trang Tran, A. Felfernig, M. Schedl

{"title":"Psychology-informed Recommender Systems","authors":"E. Lex, Dominik Kowald, Paul Seitlinger, Thi Ngoc Trang Tran, A. Felfernig, M. Schedl","doi":"10.1561/1500000090","DOIUrl":"https://doi.org/10.1561/1500000090","url":null,"abstract":"Personalized recommender systems have become indispensable in today’s online world. Most of today’s recommendation algorithms are data-driven and based on behavioral data. While such systems can produce useful recommendations, they are often uninterpretable, black-box models, which do not incorporate the underlying cognitive reasons for user behavior in the algorithms’ design. The aim of this survey is to present a thorough review of the state of the art of recommender systems that leverage psychological constructs and theories to model and predict user behavior and improve the recommendation process. We call such systems psychology-informed recommender systems. The survey identifies three categories of psychology-informed recommender systems: cognition-inspired, personality-aware, and affectaware recommender systems. Moreover, for each category, Elisabeth Lex, Dominik Kowald, Paul Seitlinger, Thi Ngoc Trang Tran, Alexander Felfernig and Markus Schedl (2021), “Psychology-informed Recommender Systems”, Foundations and Trends® in Information Retrieval: Vol. 15, No. 2, pp 134–242. DOI: 10.1561/1500000090. Full text available at: http://dx.doi.org/10.1561/1500000090","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"10 1","pages":"134-242"},"PeriodicalIF":10.4,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87436257","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}

引用次数: 32

Search and Discovery in Personal Email Collections 搜索和发现在个人电子邮件收藏

IF 10.4 2区计算机科学

Foundations and Trends in Information Retrieval Pub Date : 2021-07-05 DOI: 10.1561/1500000069

Michael Bendersky, Xuanhui Wang, Marc Najork, Donald Metzler

引用次数: 0

Fairness in Information Access Systems 信息获取系统的公平性

IF 10.4 2区计算机科学

Foundations and Trends in Information Retrieval Pub Date : 2021-05-12 DOI: 10.1561/1500000079

Michael D. Ekstrand, Anubrata Das, R. Burke, Fernando Diaz

{"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}

引用次数: 51

Search Interface Design and Evaluation 搜索界面设计与评价

IF 10.4 2区计算机科学

Foundations and Trends in Information Retrieval Pub Date : 2021-01-01 DOI: 10.1561/1500000073

Chang Liu, Ying-Hsang Liu, Jingjing Liu, R. Bierig

引用次数: 15