Search (6 results, page 1 of 1)

  • × author_ss:"Li, Y."
  • × language_ss:"e"
  1. Li, Y.; Xu, S.; Luo, X.; Lin, S.: ¬A new algorithm for product image search based on salient edge characterization (2014) 0.00 
    0.0012781365 = product of:
  0.010225092 = sum of:
    0.010225092 = product of:
      0.030675275 = sum of:
        0.030675275 = weight(_text_:problem in 1552) [ClassicSimilarity], result of:
          0.030675275 = score(doc=1552,freq=2.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.23447686 = fieldWeight in 1552, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1552)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Abstract
    Visually assisted product image search has gained increasing popularity because of its capability to greatly improve end users' e-commerce shopping experiences. Different from general-purpose content-based image retrieval (CBIR) applications, the specific goal of product image search is to retrieve and rank relevant products from a large-scale product database to visually assist a user's online shopping experience. In this paper, we explore the problem of product image search through salient edge characterization and analysis, for which we propose a novel image search method coupled with an interactive user region-of-interest indication function. Given a product image, the proposed approach first extracts an edge map, based on which contour curves are further extracted. We then segment the extracted contours into fragments according to the detected contour corners. After that, a set of salient edge elements is extracted from each product image. Based on salient edge elements matching and similarity evaluation, the method derives a new pairwise image similarity estimate. Using the new image similarity, we can then retrieve product images. To evaluate the performance of our algorithm, we conducted 120 sessions of querying experiments on a data set comprised of around 13k product images collected from multiple, real-world e-commerce websites. We compared the performance of the proposed method with that of a bag-of-words method (Philbin, Chum, Isard, Sivic, & Zisserman, 2008) and a Pyramid Histogram of Orientated Gradients (PHOG) method (Bosch, Zisserman, & Munoz, 2007). Experimental results demonstrate that the proposed method improves the performance of example-based product image retrieval.
  2. Xiao, D.; Ji, Y.; Li, Y.; Zhuang, F.; Shi, C.: Coupled matrix factorization and topic modeling for aspect mining (2018) 0.00 
    0.0012781365 = product of:
  0.010225092 = sum of:
    0.010225092 = product of:
      0.030675275 = sum of:
        0.030675275 = weight(_text_:problem in 5042) [ClassicSimilarity], result of:
          0.030675275 = score(doc=5042,freq=2.0), product of:
            0.13082431 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.030822188 = queryNorm
            0.23447686 = fieldWeight in 5042, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5042)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Abstract
    Aspect mining, which aims to extract ad hoc aspects from online reviews and predict rating or opinion on each aspect, can satisfy the personalized needs for evaluation of specific aspect on product quality. Recently, with the increase of related research, how to effectively integrate rating and review information has become the key issue for addressing this problem. Considering that matrix factorization is an effective tool for rating prediction and topic modeling is widely used for review processing, it is a natural idea to combine matrix factorization and topic modeling for aspect mining (or called aspect rating prediction). However, this idea faces several challenges on how to address suitable sharing factors, scale mismatch, and dependency relation of rating and review information. In this paper, we propose a novel model to effectively integrate Matrix factorization and Topic modeling for Aspect rating prediction (MaToAsp). To overcome the above challenges and ensure the performance, MaToAsp employs items as the sharing factors to combine matrix factorization and topic modeling, and introduces an interpretive preference probability to eliminate scale mismatch. In the hybrid model, we establish a dependency relation from ratings to sentiment terms in phrases. The experiments on two real datasets including Chinese Dianping and English Tripadvisor prove that MaToAsp not only obtains reasonable aspect identification but also achieves the best aspect rating prediction performance, compared to recent representative baselines.
  3. Crespo, J.A.; Herranz, N.; Li, Y.; Ruiz-Castillo, J.: ¬The effect on citation inequality of differences in citation practices at the web of science subject category level (2014) 0.00 
    0.0012303602 = product of:
  0.009842882 = sum of:
    0.009842882 = product of:
      0.029528644 = sum of:
        0.029528644 = weight(_text_:22 in 1291) [ClassicSimilarity], result of:
          0.029528644 = score(doc=1291,freq=4.0), product of:
            0.10793405 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.030822188 = queryNorm
            0.27358043 = fieldWeight in 1291, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1291)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Abstract
    This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.
  4. Li, Y.; Belkin, N.J.: ¬A faceted approach to conceptualizing tasks in information seeking (2008) 0.00 
    8.7789324E-4 = product of:
  0.007023146 = sum of:
    0.007023146 = product of:
      0.021069437 = sum of:
        0.021069437 = weight(_text_:29 in 2442) [ClassicSimilarity], result of:
          0.021069437 = score(doc=2442,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.19432661 = fieldWeight in 2442, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2442)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Date
    22.11.2008 16:29:21
  5. Shen, J.; Yao, L.; Li, Y.; Clarke, M.; Wang, L.; Li, D.: Visualizing the history of evidence-based medicine : a bibliometric analysis (2013) 0.00 
    8.7789324E-4 = product of:
  0.007023146 = sum of:
    0.007023146 = product of:
      0.021069437 = sum of:
        0.021069437 = weight(_text_:29 in 1090) [ClassicSimilarity], result of:
          0.021069437 = score(doc=1090,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.19432661 = fieldWeight in 1090, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1090)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Date
    28.10.2013 17:29:49
  6. Thomas, M.A.; Li, Y.; Sistenich, V.; Diango, K.N.; Kabongo, D.: ¬A multi-stakeholder engagement framework for knowledge management in ICT4D (2023) 0.00 
    8.7789324E-4 = product of:
  0.007023146 = sum of:
    0.007023146 = product of:
      0.021069437 = sum of:
        0.021069437 = weight(_text_:29 in 1086) [ClassicSimilarity], result of:
          0.021069437 = score(doc=1086,freq=2.0), product of:
            0.108422816 = queryWeight, product of:
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.030822188 = queryNorm
            0.19432661 = fieldWeight in 1086, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5176873 = idf(docFreq=3565, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1086)
      0.33333334 = coord(1/3)
  0.125 = coord(1/8)
    Date
    16.11.2023 18:55:29

Authors

Years