Search (2 results, page 1 of 1)

0.027297068 = product of:
  0.10918827 = sum of:
    0.10918827 = weight(_text_:social in 605) [ClassicSimilarity], result of:
      0.10918827 = score(doc=605,freq=10.0), product of:
        0.1847249 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046325076 = queryNorm
        0.59108585 = fieldWeight in 605, product of:
          3.1622777 = tf(freq=10.0), with freq of:
            10.0 = termFreq=10.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046875 = fieldNorm(doc=605)
  0.25 = coord(1/4)

Abstract

With the development of Web2.0, social tagging systems in which users can freely choose tags to annotate resources according to their interests have attracted much attention. In particular, literature on the emergence of collective intelligence in social tagging systems has increased. In this article, we propose a probabilistic generative model to detect latent topical communities among users. Social tags and resource contents are leveraged to model user interest in two similar and correlated ways. Our primary goal is to capture user tagging behavior and interest and discover the emergent topical community structure. The communities should be groups of users with frequent social interactions as well as similar topical interests, which would have important research implications for personalized information services. Experimental results on two real social tagging data sets with different genres have shown that the proposed generative model more accurately models user interest and detects high-quality and meaningful topical communities.

0.014386819 = product of:
  0.057547275 = sum of:
    0.057547275 = weight(_text_:social in 4244) [ClassicSimilarity], result of:
      0.057547275 = score(doc=4244,freq=4.0), product of:
        0.1847249 = queryWeight, product of:
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.046325076 = queryNorm
        0.3115296 = fieldWeight in 4244, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.9875789 = idf(docFreq=2228, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4244)
  0.25 = coord(1/4)

Abstract

Purpose The current understanding of serendipity is based primarily on studies employing westerners as the participants, and it remains uncertain whether or not this understanding would be pervasive under different cultures, such as in China. In addition, there is not a sufficient systematic investigation of context during the occurrence of serendipity in current studies. The purpose of this paper is to examine the above issues by conducting a follow-up empirical study with a group of Chinese scholars. Design/methodology/approach The social media application "WeChat" was employed as a research tool. A diary-based study was conducted and 16 participants were required to send to the researchers any cases of serendipity they encountered during a period of two weeks, and this was followed by a post-interview. Findings Chinese scholars experienced serendipity in line with the three main processes of: encountering unexpectedness, connection-making and recognising the value. An updated context-based serendipity model was constructed, where the role of context during each episode of experiencing serendipity was identified, including the external context (e.g. time, location and status), the social context and the internal context (e.g. precipitating conditions, sagacity/perceptiveness and emotion). Originality/value The updated context model provides a further understanding of the role played by context during the different processes of serendipity. The framework for experiencing serendipity has been expanded, and this may be used to classify the categories of serendipity.