A study of search intermediary working notes: implications for IR system design

This paper reports findings from an exploratory study investigating working notes created during encoding and external storage (EES) processes, by human search intermediates using a Boolean information retrieval (JR) system. EES processes have been an important area of research in educational contexts where students create and use notes to facilitate learning. In the context of interactive IR, encoding can be conceptualized as the process of creating working notes to help in the understanding and translating a user's information problem into a search strategy suitable for use with an IR system. External storage is the process of using working notes to facilitate interaction with IR systems. Analysis of 221 sets of working notes created by human search intermediaries revealed extensive use of EES processes and the creation of working notes of textual, numerical and graphical entities. Nearly 70% of recorded working notes were textual/numerical entities, nearly 30% were graphical entities and 0.73% were indiscernible. Segmentation devices were also used in 48% of the working notes. The creation of working notes during EES processes was a fundamental element within the mediated, interactive IR process. Implications for the design of IR interfaces to support users' EES processes and further research is discussed

Cross Validation Of Neural Network Applications For Automatic New Topic Identification

There are recent studies in the literature on automatic topic-shift identification in Web search engine user sessions; however most of this work applied their topic-shift identification algorithms on data logs from a single search engine. The purpose of this study is to provide the cross-validation of an artificial neural network application to automatically identify topic changes in a web search engine user session by using data logs of different search engines for training and testing the neural network. Sample data logs from the Norwegian search engine FAST (currently owned by Overture) and Excite are used in this study. Findings of this study suggest that it could be possible to identify topic shifts and continuations successfully on a particular search engine user session using neural networks that are trained on a different search engine data log

Elicitation behavior during online searching: towards a grammar of interactive information retrieval

What elicitations or requests for information do search intermediaries make of users with information requests during an information retrieval (IR) interaction -- including prior to and during an IR interaction -- and for what purpose? These issues were investigated during a study of elicitations during 40 mediated IR interactions. A total of 1557 search intermediary elicitations were identified within 15 purpose categories. The elicitation purposes of search intermediaries included requests for information on search terms and strategies, database selection, search procedures, system’s outputs and relevance of retrieved items, and users’ knowledge and previous information-seeking. These findings are compared with results from a study of end-user questions (Nahl & Tenopir, 1996) and a study of user elicitations of search intermediaries (Wu, 1993). Implications of the findings for the development of a dialogue-based model of IR interaction based on a grammar of IR interaction framework and the design of IR systems are also discussed

From highly relevant to not relevant: examining different regions of relevance

User relevance judgments are central to both the systems and user-oriented approaches to information retrieval (IR) systems research and development. User-oriented relevance research has also operated on two largely unconnected tracks. First, a relevance level track that examines users' criteria for relevance judgments. Second, a regions of relevance track that examines the measurement of users' relevance judgments. Users judgments and criteria for highly relevant items have been central issues for much of the relevance research. Findings are presented from four separate studies of relevance judgments by 55 users, conducting their initial online search on a particular information problem. In three studies, the number of items judged "partially" relevant (on a scale of relevant, partially relevant or not relevant) was positively correlated with different aspects of changes in users', including: (1) information problem definition, (2) search intermediaries' perceptions that a user's question and information problem has changed during the mediated search interaction, (3) personal knowledge due to the search interaction, and (4) criteria for making relevance judgments. Users with high knowledge and topic levels were more likely to judge items as highly relevant. Differences between users' criteria for highly, partially and non-relevant items are also identified. Findings suggest the need to expand the framework for relevance research and further identify the characteristics of the middle region of relevance or partial relevance as: (1) partially relevant items may play an important role in the early stages of a user's information seeking process over time for a particular information problem and (2) a relationship may exist between partially relevant items retrieved and changes in users' information problems during an information seeking process. Results also suggest that partially relevant items may be useful at the early stages of users' information seeking processes. We propose a useful concept of relevance as a relationship and an effect on the movement of a user through the iterative stages of their information seeking process. Users' relevance judgments can also be plotted on a three-dimensional spatial model of relevance level, region and time. Implications for the development of IR systems, searching practice and relevance research are also discussed

Investigating the Performance of Automatic New Topic Identification Across Multiple Datasets

Recent studies on automatic new topic identification in Web search engine user sessions demonstrated that neural networks are successful in automatic new topic identification. However most of this work applied their new topic identification algorithms on data logs from a single search engine. In this study, we investigate whether the application of neural networks for automatic new topic identification are more successful on some search engines than others. Sample data logs from the Norwegian search engine FAST (currently owned by Overture) and Excite are used in this study. Findings of this study suggest that query logs with more topic shifts tend to provide more successful results on shift-based performance measures, whereas logs with more topic continuations tend to provide better results on continuation-based performance measures

Reading nineteenth century schoolbooks online : user behavior in a digitized special collection

Special collections, because of the issues associated with conservation and use, a feature they share with archives, tend to be the most digitized areas in libraries. The Nineteenth Century Schoolbooks collection is a collection of 9000 rarely held nineteenth-century schoolbooks that were painstakingly collected over a lifetime of work by Prof. John A. Nietz, and donated to the Hillman Library at the University of Pittsburgh in 1958, which has since grown to 15,000. About 140 of these texts are completely digitized and showcased in a publicly accessible website through the University of Pittsburgh’s Library, along with a searchable bibliography of the entire collection, which expanded the awareness of this collection and its user base to beyond the academic community. The URL for the website is http://digital.library.pitt.edu/nietz/. The collection is a rich resource for researchers studying the intellectual, educational, and textbook publishing history of the United States. In this study, we examined several existing records collected by the Digital Research Library at the University of Pittsburgh in order to determine the identity and searching behaviors of the users of this collection. Some of the records examined include: 1) The results of a 3-month long user survey, 2) User access statistics including search queries for a period of one year, a year after the digitized collection became publicly available in 2001, and 3) E-mail input received by the website over 4 years from 2000-2004. The results of the study demonstrate the differences in online retrieval strategies used by academic researchers and historians, archivists, avocationists, and the general public, and the importance of facilitating the discovery of digitized special collections through the use of electronic finding aids and an interactive interface with detailed metadata

Towards an integrated model of information behavior

Information behavior models generally focus on one of many aspects of information behavior, either information finding, conceptualized as information seeking, information foraging or information sense-making, information organizing and information using. This ongoing study is developing an integrated model of information behavior. The research design involves a 2-week-long daily information journal self-maintained by the participants, combined with two interviews, one before, and one after the journal-keeping period. The data from the study will be analyzed using grounded theory to identify when the participants engage in the various behaviors that have already been observed, identified, and defined in previous models, in order to generate useful sequential data and an integrated model

Extending Alexander’s ecological dominance-social competition model : information behavior adaptation

Alexander’s Ecological Dominance and Social Competition (EDSC) model currently provides the most comprehensive overview of human traits in the development of a theory of human evolution and sociality (Alexander, 1990; Finn, Geary & Ward, 2005; Irons, 2005). His model provides a basis for explaining the evolution of human socio-cognitive abilities. Our paper examines the extension of Alexander’s model to incorporate the human trait of information behavior in synergy with ecological dominance and social competition as a human socio-cognitive competence. This paper discusses the various interdisciplinary perspectives exploring how evolution has shaped information behavior and why information behavior is emerging as an important human socio-cognitive competence. This paper outlines these issues, including the extension of Spink and Currier’s (2006a,b) evolution of information behavior model towards a more integrated understanding of how information behaviors have evolved (Spink & Cole, 2006)

Extending Alexander’s ecological dominance-social competition (EDSC) evolutional model

Alexander’s Ecological Dominance and Social Competition (EDSC) model currently provides the most comprehensive overview of human traits in the development of a theory of human evolution and sociality (Alexander, 1990; Finn, Geary & Ward, 2005; Irons, 2005). His model provides a basis for explaining the evolution of human socio-cognitive abilities. Our paper examines the extension of Alexander’s model to incorporate the human trait of information behavior in synergy with ecological dominance and social competition as a human socio-cognitive competence. This paper discusses the various interdisciplinary perspectives exploring how evolution has shaped information behavior and why information behavior is emerging as an important human socio-cognitive competence. This paper outlines these issues, including the extension of Spink and Currier’s (2006a,b) evolution of information behavior model towards a more integrated understanding of how information behaviors have evolved (Spink & Cole, 2006)