Recommendation, collaboration and social search

This chapter considers the social component of interactive information retrieval: what is the role of other people in searching and browsing? For simplicity we begin by considering situations without computers. After all, you can interactively retrieve information without a computer; you just have to interact with someone or something else. Such an analysis can then help us think about the new forms of collaborative interactions that extend our conceptions of information search, made possible by the growth of networked ubiquitous computing technology. Information searching and browsing have often been conceptualized as a solitary activity, however they always have a social component. We may talk about 'the' searcher or 'the' user of a database or information resource. Our focus may be on individual uses and our research may look at individual users. Our experiments may be designed to observe the behaviors of individual subjects. Our models and theories derived from our empirical analyses may focus substantially or exclusively on an individual's evolving goals, thoughts, beliefs, emotions and actions. Nevertheless there are always social aspects of information seeking and use present, both implicitly and explicitly. We start by summarizing some of the history of information access with an emphasis on social and collaborative interactions. Then we look at the nature of recommendations, social search and interfaces to support collaboration between information seekers. Following this we consider how the design of interactive information systems is influenced by their social elements

Mastering the Lawless Science of Our Law : A Story of Legal Citation Indexes

Ms. Ogden presents a history of American legal citation indexes, covering early nineteenth-century attempts, the development of modern citator systems by Frank Shepard and others, online citation systems, and the potential for future improvements in an essential tool of legal research

Do jobs follow people or people follow jobs? A meta-analysis of Carlino-Mills studies

The issue whether ‘jobs follow people’ and/or ‘people follow jobs’ has recently emerged as one of the leading themes in regional and urban science. Much of the interest herein stems from alleged inconsistencies in the empirical evidence, which naturally raises questions as for the reasons why. Arguably, the nature of causality differs across space as well as time, while speculations have been rife about a number of methodological issues that may play a crucial role in shaping the research outcomes. In this paper a preliminary attempt is described to clarify these matters, by focusing on an articulate literature of 37 so-called ‘Carlino-Mills studies’. Specifically, a statistically supported literature review, referred to as ‘meta-analysis’, is presented in which the study results are evaluated and systematically related to a variety of study characteristics that underlie these results. By listing 308 study results reported in this literature, it is revealed that the empirical evidence is conform popular belief highly inconclusive, albeit that most of the results point towards ‘jobs follow people’. The findings of the meta-regression analyses indicate that the spatial setting of the study, the adopted model specification, and variables measurement in particular affect the research outcomes that indicate the jobs-people direction of causality. No evidence is found that the examination of data referring to a particular time period, population and/ or employment group make much of a difference.

Leadership development programmes in healthcare research: a systematic review, meta-analysis and meta-aggregation

Background: Academic institutions benefit from researchers adopting leadership positions and, subsequently, leadership development programmes are of increasing importance. Despite this, no evaluation of the evidence basis for leadership development programmes for healthcare researchers has been conducted. In this study, the authors reviewed leadership development programmes for healthcare researchers and aimed to identify their impact and the factors which influenced this impact. Methods: The authors searched MEDLINE, EMBASE, CINAHL and PsycINFO between January 2000 and January 2023 for evaluations of leadership development programmes with healthcare researchers. The authors synthesised results through exploratory meta-analysis and meta-aggregation and used the Medical Education Research Study Quality Instrument (MERSQI) and Joanna Briggs Institute (JBI) Checklist for Qualitative Studies to identify higher-reliability studies. Results: 48 studies met inclusion criteria, of which approximately half (22) met the criteria for higher reliability. The median critical appraisal score was 10.5/18 for the MERSQI and 3.5/10 for the JBI. Common causes of low study quality appraisal related to study design, data analysis and reporting. Evaluations principally consisted of questionnaires measuring self-assessed outcomes. Interventions were primarily focused on junior academics. Overall, 163/168 categorised programme outcomes were positive. Coaching, experiential learning/project work and mentoring were associated with increased organisational outcomes. Conclusion: Educational methods appeared to be more important for organisational outcomes than specific educational content. To facilitate organisational outcomes, educational methods should include coaching, project work and mentoring. Programmes delivered by external faculty were less likely to be associated with organisational outcomes than those with internal or mixed faculty, but this needs further investigation. Finally, improving evaluation design will allow educators and evaluators to more effectively understand factors which are reliably associated with organisational outcomes of leadership development

Special Libraries, September 1957

Volume 48, Issue 7https://scholarworks.sjsu.edu/sla_sl_1957/1006/thumbnail.jp

Accessibility-based reranking in multimedia search engines

Traditional multimedia search engines retrieve results based mostly on the query submitted by the user, or using a log of previous searches to provide personalized results, while not considering the accessibility of the results for users with vision or other types of impairments. In this paper, a novel approach is presented which incorporates the accessibility of images for users with various vision impairments, such as color blindness, cataract and glaucoma, in order to rerank the results of an image search engine. The accessibility of individual images is measured through the use of vision simulation filters. Multi-objective optimization techniques utilizing the image accessibility scores are used to handle users with multiple vision impairments, while the impairment profile of a specific user is used to select one from the Pareto-optimal solutions. The proposed approach has been tested with two image datasets, using both simulated and real impaired users, and the results verify its applicability. Although the proposed method has been used for vision accessibility-based reranking, it can also be extended for other types of personalization context

The design and study of pedagogical paper recommendation

For learners engaging in senior-level courses, tutors in many cases would like to pick some articles as supplementary reading materials for them each week. Unlike researchers ‘Googling’ papers from the Internet, tutors, when making recommendations, should consider course syllabus and their assessment of learners along many dimensions. As such, simply ‘Googling’ articles from the Internet is far from enough. That is, learner models of each individual, including their learning interest, knowledge, goals, etc. should be considered when making paper recommendations, since the recommendation should be carried out so as to ensure that the suitability of a paper for a learner is calculated as the summation of the fitness of the appropriateness of it to help the learner in general. This type of the recommendation is called a Pedagogical Paper Recommender.In this thesis, we propose a set of recommendation methods for a Pedagogical Paper Recommender and study the various important issues surrounding it. Experimental studies confirm that making recommendations to learners in social learning environments is not the same as making recommendation to users in commercial environments such as Amazon.com. In such learning environments, learners are willing to accept items that are not interesting, yet meet their learning goals in some way or another; learners’ overall impression towards each paper is not solely dependent on the interestingness of the paper, but also other factors, such as the degree to which the paper can help to meet their ‘cognitive’ goals.It is also observed that most of the recommendation methods are scalable. Although the degree of this scalability is still unclear, we conjecture that those methods are consistent to up to 50 papers in terms of recommendation accuracy. The experiments conducted so far and suggestions made on the adoption of recommendation methods are based on the data we have collected during one semester of a course. Therefore, the generality of results needs to undergo further validation before more certain conclusion can be drawn. These follow up studies should be performed (ideally) in more semesters on the same course or related courses with more newly added papers. Then, some open issues can be further investigated. Despite these weaknesses, this study has been able to reach the research goals set out in the proposed pedagogical paper recommender which, although sounding intuitive, unfortunately has been largely ignored in the research community. Finding a ‘good’ paper is not trivial: it is not about the simple fact that the user will either accept the recommended items, or not; rather, it is a multiple step process that typically entails the users navigating the paper collections, understanding the recommended items, seeing what others like/dislike, and making decisions. Therefore, a future research goal to proceed from the study here is to design for different kinds of social navigation in order to study their respective impacts on user behavior, and how over time, user behavior feeds back to influence the system performance