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}
Michael Bendersky, Xuanhui Wang, Marc Najork, Donald Metzler
{"title":"Search and Discovery in Personal Email Collections","authors":"Michael Bendersky, Xuanhui Wang, Marc Najork, Donald Metzler","doi":"10.1561/1500000069","DOIUrl":"https://doi.org/10.1561/1500000069","url":null,"abstract":"<p>Email has been an essential communication medium for many years. As a result, the information accumulated in our mailboxes has become valuable for all of our personal and professional activities. For years, researchers have been developing interfaces, models and algorithms to facilitate search, discovery and organization of email data. In this survey, we attempt to bring together these diverse research directions, and provide both a historical background, as well as a comprehensive overview of the recent advances in the field. In particular, we lay out all the components needed in the design of a privacy-centric email search engine, including search interface, indexing, document and query understanding, retrieval, ranking and evaluation. We also go beyond search, presenting recent work on intelligent task assistance in email. Finally, we discuss some emerging trends and future directions in email search and discovery research.</p>","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"318 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2021-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138526312","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}
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}
Chang Liu, Ying-Hsang Liu, Jingjing Liu, R. Bierig
{"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":"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":"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}
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