Search (3 results, page 1 of 1)

  • × author_ss:"Xie, H."
  1. Belkin, N.J.; Cabezas, A.; Cool, C.; Kim, K.; Ng, K.B.; Park, S.; Pressman, R.; Rieh, S.; Savage, P.; Xie, H.: Rutgers interactive track at TREC-5 (1997) 0.00 
    0.0023919214 = product of:
  0.0047838427 = sum of:
    0.0047838427 = product of:
      0.009567685 = sum of:
        0.009567685 = weight(_text_:a in 3112) [ClassicSimilarity], result of:
          0.009567685 = score(doc=3112,freq=4.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18016359 = fieldWeight in 3112, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.078125 = fieldNorm(doc=3112)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Type
    a
  2. Wang, P.; Ma, Y.; Xie, H.; Wang, H.; Lu, J.; Xu, J.: "There is a gorilla holding a key on the book cover" : young children's known picture book search strategies (2022) 0.00 
    0.0023919214 = product of:
  0.0047838427 = sum of:
    0.0047838427 = product of:
      0.009567685 = sum of:
        0.009567685 = weight(_text_:a in 443) [ClassicSimilarity], result of:
          0.009567685 = score(doc=443,freq=16.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.18016359 = fieldWeight in 443, product of:
              4.0 = tf(freq=16.0), with freq of:
                16.0 = termFreq=16.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.0390625 = fieldNorm(doc=443)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    There is no information search system can assist young children's known picture book search needs since the information is not organized according to their cognitive abilities and needs. Therefore, this study explored young children's known picture book search strategies and extracted picture book search elements by simulating a search scenario and playing a picture book search game. The study found 29 elements children used to search for known picture books. Then, these elements are classified into three dimensions: The first dimension is the concept category of an element. The second dimension is an element's status in the story. The third dimension indicates where an element appears in a picture book. Additionally, it revealed a young children's general search strategy: Children first use auditory elements that they hear from the adults during reading. After receiving error returns, they add visual elements that they see by themselves in picture books. The findings can not only help to understand young children's known-item search and reformulation strategies during searching but also provide theoretical support for the development of a picture book information organization schema in the search system.
    Type
    a
  3. Xie, H.; Li, X.; Wang, T.; Lau, R.Y.K.; Wong, T.-L.; Chen, L.; Wang, F.L.; Li, Q.: Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy (2016) 0.00 
    0.001353075 = product of:
  0.00270615 = sum of:
    0.00270615 = product of:
      0.0054123 = sum of:
        0.0054123 = weight(_text_:a in 2671) [ClassicSimilarity], result of:
          0.0054123 = score(doc=2671,freq=8.0), product of:
            0.053105544 = queryWeight, product of:
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.046056706 = queryNorm
            0.10191591 = fieldWeight in 2671, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              1.153047 = idf(docFreq=37942, maxDocs=44218)
              0.03125 = fieldNorm(doc=2671)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
    Abstract
    In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.
    Type
    a

Authors

Years

Themes