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Traditional editorial effectiveness measures, such as nDCG, remain standard for Web search evaluation. Unfortunately, these traditional measures can inappropriately reward redundant information and can fail to reflect the broad range of user needs that can underlie a Web query. To address these deficiencies, several researchers have recently proposed effectiveness measures for novelty and diversity. Many of these measures are based on simple cascade models of user behavior, which operate by considering the relationship between successive elements of a result list. The properties of these measures are still poorly understood, and it is not clear from prior research that they work as intended. In this paper we examine the properties and performance of cascade measures with the goal of validating them as tools for measuring effectiveness. We explore their commonalities and differences, placing them in a unified framework; we discuss their theoretical difficulties and limitations, and compare the measures experimentally, contrasting them against traditional measures and against other approaches to measuring novelty. Data collected by the TREC 2009 Web Track is used as the basis for our experimental comparison. Our results indicate that these measures reward systems that achieve an balance between novelty and overall precision in their result lists, as intended. Nonetheless, other measures provide insights not captured by the cascade measures, and we suggest that future evaluation efforts continue to report a variety of measures.

With the increasing number and diversity of search tools available, interest in the evaluation of search systems, particularly from a user perspective, has grown among researchers. More researchers are designing and evaluating interactive information retrieval (IIR) systems and beginning to innovate in evaluation methods. Maturation of a research specialty relies on the ability to replicate research, provide standards for measurement and analysis, and understand past endeavors. This article presents a historical overview of 40 years of IIR evaluation studies using the method of systematic review. A total of 2,791 journal and conference units were manually examined and 127 articles were selected for analysis in this study, based on predefined inclusion and exclusion criteria. These articles were systematically coded using features such as author, publication date, sources and references, and properties of the research method used in the articles, such as number of subjects, tasks, corpora, and measures. Results include data describing the growth of IIR studies over time, the most frequently occurring and cited authors and sources, and the most common types of corpora and measures used. An additional product of this research is a bibliography of IIR evaluation research that can be used by students, teachers, and those new to the area. To the authors' knowledge, this is the first historical, systematic characterization of the IIR evaluation literature, including the documentation of methods and measures used by researchers in this specialty.

Classic IR (information retrieval) is inherently predicated on users searching for information, the so-called "information need". But the need behind a web search is often not informational -- it might be navigational (give me the url of the site I want to reach) or transactional (show me sites where I can perform a certain transaction, e.g. shop, download a file, or find a map). We explore this taxonomy of web searches and discuss how global search engines evolved to deal with web-specific needs.

This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average.

Understanding user intent is key to designing an effective ranking system in a search engine. In the absence of any explicit knowledge of user intent, search engines want to diversify results to improve user satisfaction. In such a setting, the probability ranking principle-based approach of presenting the most relevant results on top can be sub-optimal, and hence the search engine would like to trade-off relevance for diversity in the results. In analogy to prior work on ranking and clustering systems, we use the axiomatic approach to characterize and design diversification systems. We develop a set of natural axioms that a diversification system is expected to satisfy, and show that no diversification function can satisfy all the axioms simultaneously. We illustrate the use of the axiomatic framework by providing three example diversification objectives that satisfy different subsets of the axioms. We also uncover a rich link to the facility dispersion problem that results in algorithms for a number of diversification objectives. Finally, we propose an evaluation methodology to characterize the objectives and the underlying axioms. We conduct a large scale evaluation of our objectives based on two data sets: a data set derived from the Wikipedia disambiguation pages and a product database.

The presented ontology-based model for indexing and retrieval combines the methods and experiences of traditional indexing languages with their cognitively interpreted entities and relationships with the strengths and possibilities of formal knowledge representation. The core component of the model uses inferences along the paths of typed relations between the entities of a knowledge representation for enabling the determination of result sets in the context of retrieval processes. A proposal for a general, but condensed, inventory of typed relations is given. The entities are arranged in aspect-oriented facets to ensure a consistent hierarchical structure. The possible consequences for indexing and retrieval are discussed.

Introduction: The aim of the paper is to propose new models of information behaviour that extend the concept beyond simply information seeking to consider other modes of behaviour. The models chiefly explored are those of Wilson and Dervin. Argument: A shortcoming of some models of information behaviour is that they present a sequence of stages where it is evident that actual behaviour is not always sequential. In addition, information behaviour models tend to confine themselves to depictions of information seeking. Development: A model of "multi-directionality" is explored, to overcome the notion of sequential stages. Inspired by authors such as Chatman, Krikelas, and Savolainen, modes of information behaviour such as creating, destroying and avoiding information are included. Conclusion: New models of information behaviour are presented that replace the notion of "barriers" with the concept of "gap", as a means of integrating the views of Wilson and Dervin. The proposed models incorporate the notion of multi-directionality and identify ways in which an individual may navigate "gap" using modes of information behaviour beyond information seeking.

Purpose – This paper aims to provide an overview of principles and procedures involved in creating a faceted classification scheme for use in resource discovery in an online environment. Design/methodology/approach – Facet analysis provides an established rigorous methodology for the conceptual organization of a subject field, and the structuring of an associated classification or controlled vocabulary. This paper explains how that methodology was applied to the humanities in the FATKS project, where the objective was to explore the potential of facet analytical theory for creating a controlled vocabulary for the humanities, and to establish the requirements of a faceted classification appropriate to an online environment. A detailed faceted vocabulary was developed for two areas of the humanities within a broader facet framework for the whole of knowledge. Research issues included how to create a data model which made the faceted structure explicit and machine-readable and provided for its further development and use. Findings – In order to support easy facet combination in indexing, and facet searching and browsing on the interface, faceted classification requires a formalized data structure and an appropriate tool for its management. The conceptual framework of a faceted system proper can be applied satisfactorily to humanities, and fully integrated within a vocabulary management system. Research limitations/implications – The procedures described in this paper are concerned only with the structuring of the classification, and do not extend to indexing, retrieval and application issues. Practical implications – Many stakeholders in the domain of resource discovery consider developing their own classification system and supporting tools. The methods described in this paper may clarify the process of building a faceted classification and may provide some useful ideas with respect to the vocabulary maintenance tool. Originality/value – As far as the authors are aware there is no comparable research in this area.

This paper presents interface design recommendations for faceted navigation systems, based on 13 years of experience in experimenting with and evaluating such designs.

In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.

We study the problem of answering ambiguous web queries in a setting where there exists a taxonomy of information, and that both queries and documents may belong to more than one category according to this taxonomy. We present a systematic approach to diversifying results that aims to minimize the risk of dissatisfaction of the average user. We propose an algorithm that well approximates this objective in general, and is provably optimal for a natural special case. Furthermore, we generalize several classical IR metrics, including NDCG, MRR, and MAP, to explicitly account for the value of diversification. We demonstrate empirically that our algorithm scores higher in these generalized metrics compared to results produced by commercial search engines.

Of growing interest in the area of improving the search experience is the collection of implicit user behavior measures (implicit measures) as indications of user interest and user satisfaction. Rather than having to submit explicit user feedback, which can be costly in time and resources and alter the pattern of use within the search experience, some research has explored the collection of implicit measures as an efficient and useful alternative to collecting explicit measure of interest from users.This research article describes a recent study with two main objectives. The first was to test whether there is an association between explicit ratings of user satisfaction and implicit measures of user interest. The second was to understand what implicit measures were most strongly associated with user satisfaction. The domain of interest was Web search. We developed an instrumented browser to collect a variety of measures of user activity and also to ask for explicit judgments of the relevance of individual pages visited and entire search sessions. The data was collected in a workplace setting to improve the generalizability of the results.Results were analyzed using traditional methods (e.g., Bayesian modeling and decision trees) as well as a new usage behavior pattern analysis (“gene analysis”). We found that there was an association between implicit measures of user activity and the user's explicit satisfaction ratings. The best models for individual pages combined clickthrough, time spent on the search result page, and how a user exited a result or ended a search session (exit type/end action). Behavioral patterns (through the gene analysis) can also be used to predict user satisfaction for search sessions.

As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model supported by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world.

The facet-analytic paradigm is probably the most distinct approach to knowledge organization within Library and Information Science, and in many ways it has dominated what has be termed “modern classification theory”. It was mainly developed by S.R. Ranganathan and the British Classification Research Group, but it is mostly based on principles of logical division developed more than two millennia ago. Colon Classification (CC) and Bliss 2 (BC2) are among the most important systems developed on this theoretical basis, but it has also influenced the development of other systems, such as the Dewey Decimal Classification (DDC) and is also applied in many websites. It still has a strong position in the field and it is the most explicit and “pure” theoretical approach to knowledge organization (KO) (but it is not by implication necessarily also the most important one). The strength of this approach is its logical principles and the way it provides structures in knowledge organization systems (KOS). The main weaknesses are (1) its lack of empirical basis and (2) its speculative ordering of knowledge without basis in the development or influence of theories and socio-historical studies. It seems to be based on the problematic assumption that relations between concepts are a priori and not established by the development of models, theories and laws.

The Classification Research Group manifesto of 1955, 'Faceted classification as the basis of all information retrieval', has been at least in part achieved, and there is much evidence of faceted classification influencing a whole range of modern information retrieval tools. This paper examines the theory underlying faceted classification, how and why it has been taken up so widely, and what benefits it brings to the activity of knowledge organization. The role of facet analysis as a general research tool is also considered, and how it compares with other content analysis tools as a means of modelling subject domains.

The article describes the nature of a faceted classification, and its application in document retrieval. The kinds of facet used are illustrated. Procedures are then discussed for identifying facets in a subject field, populating the facets with individual subject terms, arranging these in helpful sequences, using the scheme to classify documents, and searching the resultant classified index, with particular reference to Internet search.

There are currently two dominant interface types for searching and browsing large image collections: keyword-based search, and searching by overall similarity to sample images. We present an alternative based on enabling users to navigate along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, in which 32 art history students explored a collection of 35,000 fine arts images, compares this approach to a standard image search interface. Despite the unfamiliarity and power of the interface (attributes that often lead to rejection of new search interfaces), the study results show that 90% of the participants preferred the metadata approach overall, 97% said that it helped them learn more about the collection, 75% found it more flexible, and 72% found it easier to use than a standard baseline system. These results indicate that a category-based approach is a successful way to provide access to image collections.

We live in an information age that requires us, more than ever, to represent, access, and use information. Over the last several decades, we have developed a modern science and technology for information retrieval, relentlessly pursuing the vision of a "memex" that Vannevar Bush proposed in his seminal article, "As We May Think." Faceted search plays a key role in this program. Faceted search addresses weaknesses of conventional search approaches and has emerged as a foundation for interactive information retrieval. User studies demonstrate that faceted search provides more effective information-seeking support to users than best-first search. Indeed, faceted search has become increasingly prevalent in online information access systems, particularly for e-commerce and site search. In this lecture, we explore the history, theory, and practice of faceted search. Although we cannot hope to be exhaustive, our aim is to provide sufficient depth and breadth to offer a useful resource to both researchers and practitioners. Because faceted search is an area of interest to computer scientists, information scientists, interface designers, and usability researchers, we do not assume that the reader is a specialist in any of these fields. Rather, we offer a self-contained treatment of the topic, with an extensive bibliography for those who would like to pursue particular aspects in more depth.