BibSonomy publications for /user/mstrohmWed Mar 17 08:51:06 CET 2010Journal of The Royal Society Interface15707-719{A general framework for analysing diversity in science, technology and society}42007categorization classification cognition tools This paper addresses the scope for more integrated general analysis of diversity in science, technology and society. It proposes a framework recognizing three necessary but individually insufficient properties of diversity. Based on 10 quality criteria, it suggests a general quantitative non-parametric diversity heuristic. This allows the systematic exploration of diversity under different perspectives, including divergent conceptions of relevant attributes and contrasting weightings on different diversity properties. It is shown how this heuristic may be used to explore different possible trade-offs between diversity and other aspects of interest, including portfolio interactions. The resulting approach offers a way to be more systematic and transparent in the treatment of scientific and technological diversity in a range of fields, including conservation management, research governance, energy policy and sustainable innovation. Thu Mar 11 15:01:44 CET 2010Trends in Cognitive ScienceApr4129-135Categorization in the wild122008TOREAD categorization taxonomy-development In studying categorization, cognitive science has focused primarily on cultural categorization, ignoring individual and institutional categorization. Because recent technological developments have made individual and institutional classification systems much more available and powerful, our understanding of the cognitive and social mechanisms that produce these systems is increasingly important. Furthermore, key aspects of categorization that have received little previous attention emerge from considering diverse types of categorization together, such as the social factors that create stability in classification systems, and the interoperability that shared conceptual systems establish between agents. Finally, the profound impact of recent technological developments on classification systems indicates that basic categorization mechanisms are highly adaptive, producing new classification systems as the situations in which they operate change.Fri Feb 26 18:08:34 CET 2010New York, NY, USACHI '09: Proceedings of the 27th international conference on Human factors in computing systems605--614An elementary social information foraging model2009information model tagging theory User interfaces and information systems have become increasingly social in recent years, aimed at supporting the decentralized, cooperative production and use of content. A theory that predicts the impact of interface and interaction designs on such factors as participation rates and knowledge discovery is likely to be useful. This paper reviews a variety of observed phenomena in social information foraging and sketches a framework extending Information Foraging Theory towards making predictions about the effects of diversity, interference, and cost-of-effort on performance time, participation rates, and utility of discoveries.Fri Feb 26 18:06:10 CET 2010New YorkInformation Foraging Theory: Adaptive Interaction with information2007INFLUENTIAL information modeling theory Although much of the hubris and hyperbole surrounding the 1990's Internet has softened to a reasonable level, the inexorable momentum of information growth continues unabated. This wealth of information provides resources for adapting to the problems posed by our increasingly complex world, but the simple availability of more information does not guarantee its successful transformation into valuable knowledge that shapes, guides, and improves our activity. When faced with something like the analysis of sense-making behavior on the web, traditional research models tell us a lot about learning and performance with browser operations, but very little about how people will actively navigate and search through information structures, what information they will choose to consume, and what conceptual models they will induce about the landscape of cyberspace. Thus, it is fortunate that a new field of research, Adaptive Information Interaction (AII), is becoming possible. AII centers on the problems of understanding and improving human-information interaction. It is about how people will best shape themselves to their information environments, and how information environments can best be shaped to people. Its roots lie in human-computer interaction (HCI), information retrieval, and the behavioral and social sciences. This book is about Information Foraging Theory (IFT), a new theory in Adaptive Information Interaction that is one example of a recent flourish of theories in adaptationist psychology that draw upon evolutionary-ecological theory in biology. IFT assumes that people (indeed, all organisms) are ecologically rational, and that human information-seeking mechanisms and strategies adapt the structure of the information environments in which they operate. Its main aim is to create technology that is better shaped to users. Information Foraging Theory will be of interest to student and professional researchers in HCI and cognitive psychology.Fri Feb 26 17:28:25 CET 2010New York, NY, USASIGWEB Newsl.Winter1--6A call for social tagging datasets2010datasets own social tagging Tagging represents a new, user-driven form of indexing and labeling resources on the web. The notion of "social tagging" usually refers to web-based systems that are supporting users in collaboratively tagging and sharing resources, such as Delicious, Flickr and others. In recent years, social tagging systems have emerged as an interesting alternative to labeling and linking resources on the web. This development has created an interesting opportunity for the Hypertext research community to gain new perspectives and understanding about the dynamics and nature of tagging and linking in large scale, participative Hypertext systems.Fri Feb 26 17:28:06 CET 2010ACM SIGWEB Hypertext'09 Graduate Student Research Challenge (Poster)The Motivation Behind Tagging2009motivation own tagging Fri Feb 26 17:25:58 CET 2010Why do users tag? detecting users’ motivation for tagging in social tagging systems2009motivation own social-factors tagging Fri Feb 26 17:24:25 CET 2010WWW2010: Proceedings of the 19th International World Wide Web Conference, Raleigh, NC, USA, April 26-30Stop Thinking, Start Tagging: Tag Semantics Emerge From Collaborative Verbosity2010motivation own semantic social tagging Tue Nov 10 17:24:11 CET 2009New York, NY, USASIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval532--539The wisdom of the few: a collaborative filtering approach based on expert opinions from the web2009motivation recommender Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach.Fri Oct 30 09:21:58 CET 2009World Wide WebDecember4421--440The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags122009MUSTREAD folksonomy goals motivation tagging Abstract  In this paper, we evaluate the effectiveness of a semantic smoothing technique to organize folksonomy tags. Folksonomy tags have no explicit relations and vary because they form uncontrolled vocabulary. We discriminates so-called subjective tagslike “cool” and “fun” from folksonomy tags without any extra knowledge other than folksonomy triples and use the level oftag generalization to form the objective tags into a hierarchy. We verify that entropy of folksonomy tags is an effectivemeasure for discriminating subjective folksonomy tags. Our hierarchical tag allocation method guarantees the number of childrennodes and increases the number of available paths to a target node compared to an existing tree allocation method for folksonomytags.Fri Oct 30 09:03:40 CET 2009Artificial Life X480-485Strategies for fast convergence in semiotic dynamics2006MUSTREAD agents simulation tagging Thu Oct 15 14:58:41 CEST 2009WSDM (Late Breaking-Results)conf/wsdm/2009Tagging with Queries: How and Why?2009query-log-analysis search tagging Mon Sep 14 20:44:17 CEST 2009Journal of Mathematical Psychology3215--233{The choice axiom after twenty years}151977MUSTREAD categorization tagging Mon Sep 14 20:43:15 CEST 2009Causal Learning: Psychology, Philosophy, and Computation{Theory unification and graphical models in human categorization}2007MUSTREAD categorization tagging Fri Aug 28 21:06:48 CEST 2009Journal of the American Society for Information Science and Technology{Twitter power: Tweets as electronic word of mouth}2009TOREAD mining twitter Thu Aug 27 02:03:21 CEST 2009Data Mining and Multi-agent Integration167--176Equipping Intelligent Agents with Commonsense Knowledge acquired from Search Query Logs: Results from an Exploratory Story 2009agents goals own query-log-analysis search Access to knowledge about user goals represents a critical component for realizing the vision of intelligent agents acting upon user intent on the web. Yet, the manual acquisition of knowledge about user goals is costly and often infeasible. Ina departure from existing approaches, this paper proposes Goal Mining as a novel perspective for knowledge acquisition. Theresearch presented in this chapter makes the following contributions: (a) it presents Goal Mining as an emerging field of research and a corresponding automatic method for the acquisition of user goals from web corpora,in the case of this paper search query logs (b) it provides insights into the nature and some characteristics of these goalsand (c) it shows that the goals acquired from query logs exhibit traits of a long tail distribution, thereby providing accessto a broad range of user goals. Our results suggest that search query logs represent a viable, yet largely untapped resource for acquiring knowledge about explicit user goals.Wed Aug 19 02:14:21 CEST 2009New York, NY, USAWWW '06: Proceedings of the 15th international conference on World Wide Web377--386A web-based kernel function for measuring the similarity of short text snippets2006SEMINAL information-retrieval similarity Determining the similarity of short text snippets, such as search queries, works poorly with traditional document similarity measures (e.g., cosine), since there are often few, if any, terms in common between two short text snippets. We address this problem by introducing a novel method for measuring the similarity between short text snippets (even those without any overlapping terms) by leveraging web search results to provide greater context for the short texts. In this paper, we define such a similarity kernel function, mathematically analyze some of its properties, and provide examples of its efficacy. We also show the use of this kernel function in a large-scale system for suggesting related queries to search engine users.Wed Aug 19 01:22:38 CEST 2009Washington, DC, USASEQUENCES '97: Proceedings of the Compression and Complexity of Sequences 199721On the Resemblance and Containment of Documents1997INFLUENTIAL information-retrieval similarity Mon Aug 17 19:30:54 CEST 2009New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web845--854P-TAG: large scale automatic generation of personalized annotation tags for the web2007simulation tagging The success of the Semantic Web depends on the availability of Web pages annotated with metadata. Free form metadata or tags, as used in social bookmarking and folksonomies, have become more and more popular and successful. Such tags are relevant keywords associated with or assigned to a piece of information (e.g., a Web page), describing the item and enabling keyword-based classification. In this paper we propose P-TAG, a method which automatically generates personalized tags for Web pages. Upon browsing a Web page, P-TAG produces keywords relevant both to its textual content, but also to the data residing on the surfer's Desktop, thus expressing a personalized viewpoint. Empirical evaluations with several algorithms pursuing this approach showed very promising results. We are therefore very confident that such a user oriented automatic tagging approach can provide large scale personalized metadata annotations as an important step towards realizing the Semantic Web.Mon Aug 10 22:47:50 CEST 2009New York, NY, USACHI '05: CHI '05 extended abstracts on Human factors in computing systems1505--1508Don't take my folders away!: organizing personal information to get things done2005MUSTREAD motivation reading-group tagging A study explores the way people organize information in support of projects ("teach a course", "plan a wedding", etc.). The folder structures to organize project information - especially electronic documents and other files - frequently resembled a "divide and conquer" problem decomposition with subfolders corresponding to major components (subprojects) of the project. Folders were clearly more than simply a means to one end: Organizing for later retrieval. Folders were information in their own right - representing, for example, a person's evolving understanding of a project and its components. Unfortunately, folders are often "overloaded" with information. For example, folders sometimes included leading characters to force an ordering ("aa", "zz"). And folder hierarchies frequently reflected a tension between organizing information for current use vs. repeated re-use.