Search (10 results, page 1 of 1)

0.022061115 = product of:
  0.08824446 = sum of:
    0.08824446 = sum of:
      0.05033082 = weight(_text_:research in 1451) [ClassicSimilarity], result of:
        0.05033082 = score(doc=1451,freq=8.0), product of:
          0.13306029 = queryWeight, product of:
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.046639 = queryNorm
          0.37825575 = fieldWeight in 1451, product of:
            2.828427 = tf(freq=8.0), with freq of:
              8.0 = termFreq=8.0
            2.8529835 = idf(docFreq=6931, maxDocs=44218)
            0.046875 = fieldNorm(doc=1451)
      0.03791364 = weight(_text_:22 in 1451) [ClassicSimilarity], result of:
        0.03791364 = score(doc=1451,freq=2.0), product of:
          0.16332182 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046639 = queryNorm
          0.23214069 = fieldWeight in 1451, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.046875 = fieldNorm(doc=1451)
  0.25 = coord(1/4)

Abstract

Research an the use of mathematical, logical, and formal methods, has been central to Information Retrieval research for a long time. Research in this area is important not only because it helps enhancing retrieval effectiveness, but also because it helps clarifying the underlying concepts of Information Retrieval. In this article we outline some of the major aspects of the subject, and summarize the papers of this special issue with respect to how they relate to these aspects. We conclude by highlighting some directions of future research, which are needed to better understand the formal characteristics of Information Retrieval.

Date

22. 3.2003 19:27:36

0.01998431 = product of:
  0.07993724 = sum of:
    0.07993724 = weight(_text_:media in 4986) [ClassicSimilarity], result of:
      0.07993724 = score(doc=4986,freq=4.0), product of:
        0.21845107 = queryWeight, product of:
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.046639 = queryNorm
        0.36592746 = fieldWeight in 4986, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4986)
  0.25 = coord(1/4)

Abstract

Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this article, we mine a massive data set of web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside "social bubbles."

0.014131041 = product of:
  0.056524165 = sum of:
    0.056524165 = weight(_text_:media in 4201) [ClassicSimilarity], result of:
      0.056524165 = score(doc=4201,freq=2.0), product of:
        0.21845107 = queryWeight, product of:
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.046639 = queryNorm
        0.25874978 = fieldWeight in 4201, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4201)
  0.25 = coord(1/4)

Abstract

Information is and always has been an elusive concept; nevertheless many philosophers, mathematicians, logicians and computer scientists have felt that it is fundamental. Many attempts have been made to come up with some sensible and intuitively acceptable definition of information; up to now, none of these have succeeded. This work is based on the approach followed by Dretske, Barwise, and Devlin, who claimed that the notion of information starts from the position that given an ontology of objects individuated by a cognitive agent, it makes sense to speak of the information an object (e.g., a text, an image, a video) contains about another object (e.g. the query). This phenomenon is captured by the flow of information between objects. Its exploitation is the task of an information retrieval system. These authors proposes a theory of information that provides an analysis of the concept of information (any type, from any media) and the manner in which intelligent organisms (referring to as cognitive agents) handle and respond to the information picked up from their environment. They defined the nature of information flow and the mechanisms that give rise to such a flow. The theory, which is based on Situation Theory, is expressed with a calculus defined on channels. The calculus was defined so that it satisfies properties that are attributes to information and its flows. This paper demonstrates the connection between this calculus and information retrieval, and porposes a model of an information retrieval system based on this calculus

0.014131041 = product of:
  0.056524165 = sum of:
    0.056524165 = weight(_text_:media in 1497) [ClassicSimilarity], result of:
      0.056524165 = score(doc=1497,freq=2.0), product of:
        0.21845107 = queryWeight, product of:
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.046639 = queryNorm
        0.25874978 = fieldWeight in 1497, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1497)
  0.25 = coord(1/4)

Abstract

Online content providers, such as news portals and social media platforms, constantly seek new ways to attract large shares of online attention by keeping their users engaged. A common challenge is to identify which aspects of online interaction influence user engagement the most. In this article, through an analysis of a news article collection obtained from Yahoo News US, we demonstrate that news articles exhibit considerable variation in terms of the sentimentality and polarity of their content, depending on factors such as news provider and genre. Moreover, through a laboratory study, we observe the effect of sentimentality and polarity of news and comments on a set of subjective and objective measures of engagement. In particular, we show that attention, affect, and gaze differ across news of varying interestingness. As part of our study, we also explore methods that exploit the sentiments expressed in user comments to reorder the lists of comments displayed in news pages. Our results indicate that user engagement can be anticipated predicted if we account for the sentimentality and polarity of the content as well as other factors that drive attention and inspire human curiosity.

0.014131041 = product of:
  0.056524165 = sum of:
    0.056524165 = weight(_text_:media in 3595) [ClassicSimilarity], result of:
      0.056524165 = score(doc=3595,freq=2.0), product of:
        0.21845107 = queryWeight, product of:
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.046639 = queryNorm
        0.25874978 = fieldWeight in 3595, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3595)
  0.25 = coord(1/4)

Abstract

Prominent news sites on the web provide hundreds of news articles daily. The abundance of news content competing to attract online attention, coupled with the manual effort involved in article selection, necessitates the timely prediction of future popularity of these news articles. The future popularity of a news article can be estimated using signals indicating the article's penetration in social media (e.g., number of tweets) in addition to traditional web analytics (e.g., number of page views). In practice, it is important to make such estimations as early as possible, preferably before the article is made available on the news site (i.e., at cold start). In this paper we perform a study on cold-start news popularity prediction using a collection of 13,319 news articles obtained from Yahoo News, a major news provider. We characterize the popularity of news articles through a set of online metrics and try to predict their values across time using machine learning techniques on a large collection of features obtained from various sources. Our findings indicate that predicting news popularity at cold start is a difficult task, contrary to the findings of a prior work on the same topic. Most articles' popularity may not be accurately anticipated solely on the basis of content features, without having the early-stage popularity values.

0.014131041 = product of:
  0.056524165 = sum of:
    0.056524165 = weight(_text_:media in 4289) [ClassicSimilarity], result of:
      0.056524165 = score(doc=4289,freq=2.0), product of:
        0.21845107 = queryWeight, product of:
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.046639 = queryNorm
        0.25874978 = fieldWeight in 4289, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.6838713 = idf(docFreq=1110, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4289)
  0.25 = coord(1/4)

0.0052427943 = product of:
  0.020971177 = sum of:
    0.020971177 = product of:
      0.041942354 = sum of:
        0.041942354 = weight(_text_:research in 4083) [ClassicSimilarity], result of:
          0.041942354 = score(doc=4083,freq=2.0), product of:
            0.13306029 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.046639 = queryNorm
            0.31521314 = fieldWeight in 4083, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.078125 = fieldNorm(doc=4083)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Source

SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a

0.0036699555 = product of:
  0.014679822 = sum of:
    0.014679822 = product of:
      0.029359644 = sum of:
        0.029359644 = weight(_text_:research in 2267) [ClassicSimilarity], result of:
          0.029359644 = score(doc=2267,freq=2.0), product of:
            0.13306029 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.046639 = queryNorm
            0.22064918 = fieldWeight in 2267, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2267)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Series

European research letter

0.0026213971 = product of:
  0.010485589 = sum of:
    0.010485589 = product of:
      0.020971177 = sum of:
        0.020971177 = weight(_text_:research in 5169) [ClassicSimilarity], result of:
          0.020971177 = score(doc=5169,freq=2.0), product of:
            0.13306029 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.046639 = queryNorm
            0.15760657 = fieldWeight in 5169, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5169)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

0.0026213971 = product of:
  0.010485589 = sum of:
    0.010485589 = product of:
      0.020971177 = sum of:
        0.020971177 = weight(_text_:research in 3529) [ClassicSimilarity], result of:
          0.020971177 = score(doc=3529,freq=2.0), product of:
            0.13306029 = queryWeight, product of:
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.046639 = queryNorm
            0.15760657 = fieldWeight in 3529, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              2.8529835 = idf(docFreq=6931, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3529)
      0.5 = coord(1/2)
  0.25 = coord(1/4)

Footnote

This work was done while Janette Lehmann was a PhD student at Universitat Pompeu Fabra and it was carried out as part of her PhD internship at Yahoo! Labs Barcelona. This work was carried out while Carlos Castillo was working at Qatar Computing Research Institute.