Search (4 results, page 1 of 1)

0.009611183 = product of:
  0.019222366 = sum of:
    0.019222366 = product of:
      0.03844473 = sum of:
        0.03844473 = weight(_text_:k in 1808) [ClassicSimilarity], result of:
          0.03844473 = score(doc=1808,freq=2.0), product of:
            0.16245733 = queryWeight, product of:
              3.569778 = idf(docFreq=3384, maxDocs=44218)
              0.045509085 = queryNorm
            0.23664509 = fieldWeight in 1808, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.569778 = idf(docFreq=3384, maxDocs=44218)
              0.046875 = fieldNorm(doc=1808)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

0.008009318 = product of:
  0.016018637 = sum of:
    0.016018637 = product of:
      0.032037273 = sum of:
        0.032037273 = weight(_text_:k in 4938) [ClassicSimilarity], result of:
          0.032037273 = score(doc=4938,freq=2.0), product of:
            0.16245733 = queryWeight, product of:
              3.569778 = idf(docFreq=3384, maxDocs=44218)
              0.045509085 = queryNorm
            0.19720423 = fieldWeight in 4938, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.569778 = idf(docFreq=3384, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4938)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Tags associated with social images are valuable information source for superior image search and retrieval experiences. Although various heuristics are valuable to boost tag-based search for images, there is a lack of general framework to study the impact of these heuristics. Specifically, the task of ranking images matching a given tag query based on their associated tags in descending order of relevance has not been well studied. In this article, we take the first step to propose a generic, flexible, and extensible framework for this task and exploit it for a systematic and comprehensive empirical evaluation of various methods for ranking images. To this end, we identified five orthogonal dimensions to quantify the matching score between a tagged image and a tag query. These five dimensions are: (i) tag relatedness to measure the degree of effectiveness of a tag describing the tagged image; (ii) tag discrimination to quantify the degree of discrimination of a tag with respect to the entire tagged image collection; (iii) tag length normalization analogous to document length normalization in web search; (iv) tag-query matching model for the matching score computation between an image tag and a query tag; and (v) query model for tag query rewriting. For each dimension, we identify a few implementations and evaluate their impact on NUS-WIDE dataset, the largest human-annotated dataset consisting of more than 269K tagged images from Flickr. We evaluated 81 single-tag queries and 443 multi-tag queries over 288 search methods and systematically compare their performances using standard metrics including Precision at top-K, Mean Average Precision (MAP), Recall, and Normalized Discounted Cumulative Gain (NDCG).

0.008009318 = product of:
  0.016018637 = sum of:
    0.016018637 = product of:
      0.032037273 = sum of:
        0.032037273 = weight(_text_:k in 967) [ClassicSimilarity], result of:
          0.032037273 = score(doc=967,freq=2.0), product of:
            0.16245733 = queryWeight, product of:
              3.569778 = idf(docFreq=3384, maxDocs=44218)
              0.045509085 = queryNorm
            0.19720423 = fieldWeight in 967, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.569778 = idf(docFreq=3384, maxDocs=44218)
              0.0390625 = fieldNorm(doc=967)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Abstract

Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.

0.007707316 = product of:
  0.015414632 = sum of:
    0.015414632 = product of:
      0.030829264 = sum of:
        0.030829264 = weight(_text_:22 in 5274) [ClassicSimilarity], result of:
          0.030829264 = score(doc=5274,freq=2.0), product of:
            0.15936506 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.045509085 = queryNorm
            0.19345059 = fieldWeight in 5274, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5274)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 7.2006 16:18:25