The Experts below are selected from a list of 6009 Experts worldwide ranked by ideXlab platform
Mark Sanderson - One of the best experts on this subject based on the ideXlab platform.
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analyzing geographic query reformulation an exploratory study
Journal of the Association for Information Science and Technology, 2014Co-Authors: Saad Aloteibi, Mark SandersonAbstract:Search Engine users typically engage in multiquery sessions in their quest to fulfill their information needs. Despite a plethora of reSearch findings suggesting that a significant group of users look for information within a specific geographical scope, existing reformulation studies lack a focused analysis of how users reformulate geographic queries. This study comprehensively investigates the ways in which users reformulate such needs in an attempt to fill this gap in the literature. Reformulated sessions were sampled from a query log of a Major Search Engine to extract 2,400 entries that were manually inspected to filter geo sessions. This filter identified 471 Search sessions that included geographical intent, and these sessions were analyzed quantitatively and qualitatively. The results revealed that one in five of the users who reformulated their queries were looking for geographically related information. They reformulated their queries by changing the content of the query rather than the structure. Users were not following a unified sequence of modifications and instead performed a single reformulation action. However, in some cases it was possible to anticipate their next move. A number of tasks in geo modifications were identified, including standard, multi-needs, multi-places, and hybrid approaches. The reSearch concludes that it is important to specialize query reformulation studies to focus on particular query types rather than generically analyzing them, as it is apparent that geographic queries have their special reformulation characteristics.
Lucy A Peipins - One of the best experts on this subject based on the ideXlab platform.
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cancer internet Search activity on a Major Search Engine united states 2001 2003
Journal of Medical Internet Research, 2005Co-Authors: Crystale Purvis Cooper, Kenneth P Mallon, Steven Leadbetter, Lori A Pollack, Lucy A PeipinsAbstract:BACKGROUND: To locate online health information, Internet users typically use a Search Engine, such as Yahoo! or Google. We studied Yahoo! Search activity related to the 23 most common cancers in the United States. OBJECTIVE: The objective was to test three potential correlates of Yahoo! cancer Search activity—estimated cancer incidence, estimated cancer mortality, and the volume of cancer news coverage—and to study the periodicity of and peaks in Yahoo! cancer Search activity. METHODS: Yahoo! cancer Search activity was obtained from a proprietary database called the Yahoo! Buzz Index. The American Cancer Society's estimates of cancer incidence and mortality were used. News reports associated with specific cancer types were identified using the LexisNexis “US News” database, which includes more than 400 national and regional newspapers and a variety of newswire services. RESULTS: The Yahoo! Search activity associated with specific cancers correlated with their estimated incidence (Spearman rank correlation, ρ = 0.50, P = .015), estimated mortality (ρ = 0.66, P = .001), and volume of related news coverage (ρ = 0.88, P < .001). Yahoo! cancer Search activity tended to be higher on weekdays and during national cancer awareness months but lower during summer months; cancer news coverage also tended to follow these trends. Sharp increases in Yahoo! Search activity scores from one day to the next appeared to be associated with increases in relevant news coverage. CONCLUSIONS: Media coverage appears to play a powerful role in prompting online Searches for cancer information. Internet Search activity offers an innovative tool for passive surveillance of health information–seeking behavior. [J Med Internet Res 2005;7(3):e36]
Hana Shepherd - One of the best experts on this subject based on the ideXlab platform.
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Emergence of Consensus and Shared Vocabularies in Collaborative Tagging Systems
2010Co-Authors: Valentin Robu, Harry Halpin, Hana ShepherdAbstract:This paper uses data from the social bookmarking site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus ” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed both for the most utilized tags at the top of tag distributions and the so-called “long tail.” Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a Major Search Engine. Although the empirical analysis presented in this paper is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across all websites that employ tagging. Categories and Subject Descriptors: H.5.3 [Group and organizational interfaces]: Collaborative computing
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emergence of consensus and shared vocabularies in collaborative tagging systems
ACM Transactions on The Web, 2009Co-Authors: Valentin Robu, Harry Halpin, Hana ShepherdAbstract:This article uses data from the social bookmarking site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed for both the most utilized tags at the top of tag distributions and the so-called long tail. Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a Major Search Engine. Although the empirical analysis presented in this article is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across other websites that employ tagging.
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Emergence of consensus and shared vocabularies in collaborative tagging systems
2009Co-Authors: Valentin Robu, Harry Halpin, Hana ShepherdAbstract:This paper uses data from the social bookmarking site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus ” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed both for the most utilized tags at the top of tag distributions and the so-called “long tail.” Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a Major Search Engine
Pingzhong Tang - One of the best experts on this subject based on the ideXlab platform.
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reinforcement mechanism design with applications to dynamic pricing in sponsored Search auctions
National Conference on Artificial Intelligence, 2020Co-Authors: Weiran Shen, Binghui Peng, Hanpeng Liu, Michael Zhang, Ruohan Qian, Zhi Guo, Zongyao Ding, Yan Hong, Pingzhong TangAbstract:In many social systems in which individuals and organizations interact with each other, there can be no easy laws to govern the rules of the environment, and agents' payoffs are often influenced by other agents' actions. We examine such a social system in the setting of sponsored Search auctions and tackle the Search Engine's dynamic pricing problem by combining the tools from both mechanism design and the AI domain. In this setting, the environment not only changes over time, but also behaves strategically. Over repeated interactions with bidders, the Search Engine can dynamically change the reserve prices and determine the optimal strategy that maximizes the profit. We first train a buyer behavior model, with a real bidding data set from a Major Search Engine, that predicts bids given information disclosed by the Search Engine and the bidders' performance data from previous rounds. We then formulate the dynamic pricing problem as an MDP and apply a reinforcement-based algorithm that optimizes reserve prices over time. Experiments demonstrate that our model outperforms static optimization strategies including the ones that are currently in use as well as several other dynamic ones.
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reinforcement mechanism design with applications to dynamic pricing in sponsored Search auctions
arXiv: Computer Science and Game Theory, 2017Co-Authors: Weiran Shen, Binghui Peng, Michael Zhang, Ruohan Qian, Zongyao Ding, Yan Hong, Pengjun Lu, Pingzhong TangAbstract:In this study, we apply reinforcement learning techniques and propose what we call reinforcement mechanism design to tackle the dynamic pricing problem in sponsored Search auctions. In contrast to previous game-theoretical approaches that heavily rely on rationality and common knowledge among the bidders, we take a data-driven approach, and try to learn, over repeated interactions, the set of optimal reserve prices. We implement our approach within the current sponsored Search framework of a Major Search Engine: we first train a buyer behavior model, via a real bidding data set, that accurately predicts bids given information that bidders are aware of, including the game parameters disclosed by the Search Engine, as well as the bidders' KPI data from previous rounds. We then put forward a reinforcement/MDP (Markov Decision Process) based algorithm that optimizes reserve prices over time, in a GSP-like auction. Our simulations demonstrate that our framework outperforms static optimization strategies including the ones that are currently in use, as well as several other dynamic ones.
Saad Aloteibi - One of the best experts on this subject based on the ideXlab platform.
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analyzing geographic query reformulation an exploratory study
Journal of the Association for Information Science and Technology, 2014Co-Authors: Saad Aloteibi, Mark SandersonAbstract:Search Engine users typically engage in multiquery sessions in their quest to fulfill their information needs. Despite a plethora of reSearch findings suggesting that a significant group of users look for information within a specific geographical scope, existing reformulation studies lack a focused analysis of how users reformulate geographic queries. This study comprehensively investigates the ways in which users reformulate such needs in an attempt to fill this gap in the literature. Reformulated sessions were sampled from a query log of a Major Search Engine to extract 2,400 entries that were manually inspected to filter geo sessions. This filter identified 471 Search sessions that included geographical intent, and these sessions were analyzed quantitatively and qualitatively. The results revealed that one in five of the users who reformulated their queries were looking for geographically related information. They reformulated their queries by changing the content of the query rather than the structure. Users were not following a unified sequence of modifications and instead performed a single reformulation action. However, in some cases it was possible to anticipate their next move. A number of tasks in geo modifications were identified, including standard, multi-needs, multi-places, and hybrid approaches. The reSearch concludes that it is important to specialize query reformulation studies to focus on particular query types rather than generically analyzing them, as it is apparent that geographic queries have their special reformulation characteristics.
