The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

Linked: The New Science of Networks

In just a short period of time, social media have altered many aspects of our daily lives, from how we form and maintain social relationships to how we discover, access and share information online. Now social media are also affecting how we teach and learn. In this paper, we discuss methods that can help researchers and educators evaluate and understand the observed and potential use of social media for teaching and learning through content and network analyses of social media texts and networks. This paper is based on a workshop given at the 2014 Learning Analytics and Knowledge conference, and presents an overview of the measures and potential of a multi-method approach for studying learning via social media. The theoretical discussion is augmented with study of the case of Twitter discussion from a cMOOC class.

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Analyzing Social Media And Learning Through Content And Social Network Analysis: A Faceted Methodological Approach

ABSTRACT Inclusion of open resources that employ a peer-generated approach is changing who learns what, from whom, and via what means. With these changes, there is a shift in responsibilities from the course designer to motivated and self-directed learner-participants. While much research on e-learning has addressed challenges of creating and sustaining participatory environments, the development of massive open online courses calls for new approaches that go beyond the existing research on participatory environments in institutionally defined classes. We decenter institutionally defined classes and broaden the discussion to the literature on the creation of open virtual communities and the operation of open online crowds. We draw on literatures on online organizing, learning science, and emerging educational practice to discuss how collaboration and peer production shape learning and enable “crowdsourcing the curriculum.”

Crowdsourcing the Curriculum: Redefinin g E- Learning Practices Thr ough Peer-Generated Approaches

In the context of document retrieval in the biomedical domain, this paper explores the complex relationship between the quality of initial query results and the overall utility of an interactive retrieval system. We demonstrate that a content-similarity browsing tool can compensate for poor retrieval results, and that the relationship between retrieval performance and overall utility is non-linear. Arguments are advanced with user simulations, which characterize the relevance of documents that a user might encounter with different browsing strategies. With broader implications to IR, this work provides a case study of how user simulations can be exploited as a formative tool for automatic utility evaluation. Simulation-based studies provide researchers with an additional evaluation tool to complement interactive and Cranfield-style experiments.

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How do users find things with PubMed?: towards automatic utility evaluation with user simulations

In just a short period of time, social media have altered many aspects of our daily lives, from how we form and maintain social relationships to how we discover, access and share information online. Now social media are also beginning to affect how we teach and learn in this increasingly interconnected and information-rich world. The panelists will discuss their ongoing work that seeks to understand the affordances and potential roles of social media in learning, as well as to determine and provide methods that can help researchers and educators evaluate the use of social media for teaching and learning based on automated analyses of social media texts and networks. The panel will focus on the first phase of this five-year research initiative "Learning Analytics for the Social Media Age" funded by the Social Science and Humanites Research Council of Canada (2013--2018).

Learning analytics for the social media age

Information management systems improve the retention of information in large collections. As such they act as memory prostheses, implying an ideal basis in human memory models. Since humans process information by association, and situate it in the context of space and time, systems should maximize their effectiveness by mimicking these functions. Since human attentional capacity is limited, systems should scaffold cognitive efforts in a comprehensible manner. We propose the Principles of Mnemonic Associative Knowledge (P-MAK), which describes a framework for semantically identifying, organizing, and retrieving information, and for encoding episodic events by time and stimuli. Inspired by prominent human memory models, we propose associative networks as a preferred representation. Networks are ideal for their parsimony, flexibility, and ease of inspection. Networks also possess topological properties--such as clusters, hubs, and the small world--that aid analysis and navigation in an information space. Our cognitive perspective addresses fundamental problems faced by information management systems, in particular the retrieval of related items and the representation of context. We present evidence from neuroscience and memory research in support of this approach, and discuss the implications of systems design within the constraints of P-MAK's principles, using text documents as an illustrative semantic domain.

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Cognitive Principles for Information Management: The Principles of Mnemonic Associative Knowledge (P-MAK)

Hypertext allows users to navigate between related materials in digital libraries. The most fundamental automated hypertexts are those constructed on the basis of semantic similarity. Such hypertexts have been evaluated by a variety of means, but seldom by real users given simulated real-world tasks. We claim that while other methods exist, one of the best ways to prove the usefulness of hypertext is to show the benefits for users performing realistic tasks. We compare the reformulation of queries that users perform in keyword searching, to the query reformulation implicit in browsing between documents linked by similarity of content. We find that a static automatically-constructed similarity hypertext provides useful linking between related items, improving the retrieval of targets when used to augment standard keyword search.

Static reformulation: a user study of static hypertext for query-based reformulation

For a collection of digitized monographs in a subject domain, a domain meta-index provides a summary of domain concepts, and a structured vocabulary to support a scholar's navigation and search. We present a prototype of a Meta-index User Interface (MUI) that provides views of a domain at three levels: summarizing and comparing domains, exposing the regularities of a domain's vocabulary, and displaying book information and page content related both to objectively-representative books, and to specific user searches.

Dynamic online views of meta-indexes

Information management systems act as a prosthetic scaffold for human memory. They retain and organize information objects to be conveniently recalled in support of knowledge-based tasks. We note a striking similarity between the functions of human memory and the processes in computational information retrieval. For this reason, we ask whether it is viable to purposely design information management systems biomimetically, i.e., in a manner inspired by biological systems. 
Based on a comparison of cognitive models of human memory and computational information retrieval algorithms, we propose the Principles of Mnemonic Associative Knowledge (P-MAK) to describe the necessary components of biomimetic systems: the constraints of computing machines, the properties of human memory, how semantic knowledge representations are constructed, and the contexts in which information is usefully retrieved. The goal of P-MAK is to describe systems that are simple, inspectable, comprehensible, and easy to use. 
Since human memory as described by cognitive network models is analogous to a large associative hypertext repository, P-MAK's principles suggest that networks would be an appropriate representation format. Therefore, we build a semantic similarity network from a document corpus using information retrieval (IR) algorithms, and describe how these processes are comparable to the functions of human semantic memory. To approximate an optimal link distribution, we introduce a novel link-pruning technique to tune the network to a small-world topology. We show in a user study that a semantic network based on cognitive models can improve user access to information. 
The ability to recall information in appropriate contexts is also a useful property of human memory. Based on models of human episodic memory, we propose a real-time, incremental temporal index that captures some of the regularity of human information behaviour. Temporal patterns are represented using a novel cue-event-object (CEO) model, in which observed events are related to a collection of cues. The cues describe time, place, or sensory qualities and are analogous to cognitive schemas . Cues are combined to represent an event, analogous to cognitive convergence zones. The model connects related cues, events, and objects together to encode the relations present in observed occurrences. The CEO model simulates cognitive reinforcement learning to build patterns of user information behaviour. If an object is used consistently at a given time, the links connecting cues, event, and object all grow stronger; otherwise, they decay and are "forgotten". The resulting network structure can function as a recommender system by using spreading activation to retrieve objects at times and under circumstances where they have previously proven themselves useful. The model also allows users to pose queries such as when an event typically occurs, or what items are used at particular times. In a user-log experiment, we show that the CEO model quickly learns to make correct predictions of user behaviour, and increases in accuracy the more data that it is given.

Michael Huggett

Papers

Learning analytics for the social media age

Cognitive Principles for Information Management: The Principles of Mnemonic Associative Knowledge (P-MAK)

Static reformulation: a user study of static hypertext for query-based reformulation

Dynamic online views of meta-indexes

Biomimetic information retrieval with spreading-activation networks