209,721 research outputs found

    Community Interest as An Indicator for Ranking

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    Ranking documents in response to users\u27 information needs is a challenging task, due, in part, to the dynamic nature of users\u27 interests with respect to a query. We hypothesize that the interests of a given user are similar to the interests of the broader community of which he or she is a part and propose an innovative method that uses social media to characterize the interests of the community and use this characterization to improve future rankings. By generating a community interest vector (CIV) and community interest language model (CILM) for a given query, we use community interest to alter the ranking score of individual documents retrieved by the query. The CIV or CILM is based on a continuously updated set of recent (daily or past few hours) user oriented text data. The interest based ranking method is evaluated by using Amazon Turk to against relevance based ranking and search engines\u27 ranking results. Overall, the experiment result shows community interest is an effective indicator for dynamic ranking

    A Netnographic Study of Entrepreneurial Traits: Evaluating classic typologies using the crowdsourcing algorithm of an online community

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    This paper evaluates how the advices of experienced entrepreneurs to young start-up creators in an online community reflect entrepreneurship traits commonly found in conceptual typologies. The overall goal is to contrast and evaluate existing models based on evidence from an online community. This should facilitate future studies to improve current typologies by ranking entrepreneurial traits according to perceived relevance. In order to achieve these objectives, we have conducted a “netnographic study” (i.e., the qualitative analysis of web-based content) of 96 answers to the question “What is the best advice for a young, first-time startup CEO?” on Quora.com. Relying on Quora’s ranking algorithm (based on crowdsourcing of votes and community prestige), we focused on the top 50% of answers (which we shall call the “above Quora 50” category) considered the most relevant by its 2000+ followers and 120,000+ viewers. We used Nvivo as a Qualitative Data Analysis Software to code all the entries into the literature categories. These codes were then later retrieved using matrix queries to compare the incidence of traits and the perceived relevance of answers. We found that among the 50% highest ranking answers on Quora, the following traits are perceived as the most important for young entrepreneurs to develop: management style, attitude in interpersonal relations, vision, self-concept, leadership style, marketing, market and customer knowledge, innovation, technical knowledge and skills, attitude to growth, ability to adapt, purpose and relations system. These results could lead to improving existing typologies and creating new models capable of better identifying people with the highest potential to succeed in new venture creation

    Research priorities in immersive learning technology: the perspectives of the iLRN community

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    This paper presents the perspectives of the immersive learning research network community on the relevance of various challenges to the adoption of immersive learning technology, along three dimensions: access, content production, and deployment. Using a previously validated questionnaire, we surveyed this community of 622 researchers and practitioners during the summer of 2018, attaining 54 responses. By ranking the challenges individually and within each dimension, the results point towards higher relevance being placed on aspects that link immersive environments with learning management systems and pedagogical tasks, alongside aspects that empower non-technical users (educational actors) to produce interactive stories, objects, and characters.The work presented herein has been partially funded under the European H2020 program H2020-ICT-2015, BEACONING project, grant agreement nr. 687676.info:eu-repo/semantics/acceptedVersio

    The Role and Relevance of Rankings in Higher Education Policymaking

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    Explores the rise of college rankings, similarities and differences from postsecondary assessment efforts, and factors behind their limited relevance to policy such as their effect on institutional behaviors. Recommends ways to enhance policy relevance

    Evaluation Measures for Relevance and Credibility in Ranked Lists

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    Recent discussions on alternative facts, fake news, and post truth politics have motivated research on creating technologies that allow people not only to access information, but also to assess the credibility of the information presented to them by information retrieval systems. Whereas technology is in place for filtering information according to relevance and/or credibility, no single measure currently exists for evaluating the accuracy or precision (and more generally effectiveness) of both the relevance and the credibility of retrieved results. One obvious way of doing so is to measure relevance and credibility effectiveness separately, and then consolidate the two measures into one. There at least two problems with such an approach: (I) it is not certain that the same criteria are applied to the evaluation of both relevance and credibility (and applying different criteria introduces bias to the evaluation); (II) many more and richer measures exist for assessing relevance effectiveness than for assessing credibility effectiveness (hence risking further bias). Motivated by the above, we present two novel types of evaluation measures that are designed to measure the effectiveness of both relevance and credibility in ranked lists of retrieval results. Experimental evaluation on a small human-annotated dataset (that we make freely available to the research community) shows that our measures are expressive and intuitive in their interpretation

    A study into annotation ranking metrics in geo-tagged image corpora

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    Community contributed datasets are becoming increasingly common in automated image annotation systems. One important issue with community image data is that there is no guarantee that the associated metadata is relevant. A method is required that can accurately rank the semantic relevance of community annotations. This should enable the extracting of relevant subsets from potentially noisy collections of these annotations. Having relevant, non heterogeneous tags assigned to images should improve community image retrieval systems, such as Flickr, which are based on text retrieval methods. In the literature, the current state of the art approach to ranking the semantic relevance of Flickr tags is based on the widely used tf-idf metric. In the case of datasets containing landmark images, however, this metric is inefficient due to the high frequency of common landmark tags within the data set and can be improved upon. In this paper, we present a landmark recognition framework, that provides end-to-end automated recognition and annotation. In our study into automated annotation, we evaluate 5 alternate approaches to tf-idf to rank tag relevance in community contributed landmark image corpora. We carry out a thorough evaluation of each of these ranking metrics and results of this evaluation demonstrate that four of these proposed techniques outperform the current commonly-used tf-idf approach for this task
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