Search (7 results, page 1 of 1)

Zhang, X.; Liu, J.; Cole, M.; Belkin, N.: Predicting users' domain knowledge in information retrieval using multiple regression analysis of search behaviors (2015) 0.01
```
0.01168419 = product of:
  0.02336838 = sum of:
    0.02336838 = product of:
      0.04673676 = sum of:
        0.04673676 = weight(_text_:searching in 1822) [ClassicSimilarity], result of:
          0.04673676 = score(doc=1822,freq=2.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.22347288 = fieldWeight in 1822, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1822)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
```
Abstract

User domain knowledge affects search behaviors and search success. Predicting a user's knowledge level from implicit evidence such as search behaviors could allow an adaptive information retrieval system to better personalize its interaction with users. This study examines whether user domain knowledge can be predicted from search behaviors by applying a regression modeling analysis method. We identify behavioral features that contribute most to a successful prediction model. A user experiment was conducted with 40 participants searching on task topics in the domain of genomics. Participant domain knowledge level was assessed based on the users' familiarity with and expertise in the search topics and their knowledge of MeSH (Medical Subject Headings) terms in the categories that corresponded to the search topics. Users' search behaviors were captured by logging software, which includes querying behaviors, document selection behaviors, and general task interaction behaviors. Multiple regression analysis was run on the behavioral data using different variable selection methods. Four successful predictive models were identified, each involving a slightly different set of behavioral variables. The models were compared for the best on model fit, significance of the model, and contributions of individual predictors in each model. Each model was validated using the split sampling method. The final model highlights three behavioral variables as domain knowledge level predictors: the number of documents saved, the average query length, and the average ranking position of the documents opened. The results are discussed, study limitations are addressed, and future research directions are suggested.
Liu, J.; Liu, C.; Belkin, N.J.: Predicting information searchers' topic knowledge at different search stages (2016) 0.01
```
0.01168419 = product of:
  0.02336838 = sum of:
    0.02336838 = product of:
      0.04673676 = sum of:
        0.04673676 = weight(_text_:searching in 3147) [ClassicSimilarity], result of:
          0.04673676 = score(doc=3147,freq=2.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.22347288 = fieldWeight in 3147, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3147)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
```
Abstract

As a significant contextual factor in information search, topic knowledge has been gaining increased research attention. We report on a study of the relationship between information searchers' topic knowledge and their search behaviors, and on an attempt to predict searchers' topic knowledge from their behaviors during the search. Data were collected in a controlled laboratory experiment with 32 undergraduate journalism student participants, each searching on 4 tasks of different types. In general, behavioral variables were not found to have significant differences between users with high and low levels of topic knowledge, except the mean first dwell time on search result pages. Several models were built to predict topic knowledge using behavioral variables calculated at 3 different stages of search episodes: the first-query-round, the middle point of the search, and the end point. It was found that a model using some search behaviors observed in the first query round led to satisfactory prediction results. The results suggest that early-session search behaviors can be used to predict users' topic knowledge levels, allowing personalization of search for users with different levels of topic knowledge, especially in order to assist users with low topic knowledge.
Liu, J.; Zhang, X.: ¬The role of domain knowledge in document selection from search results (2019) 0.01
```
0.01168419 = product of:
  0.02336838 = sum of:
    0.02336838 = product of:
      0.04673676 = sum of:
        0.04673676 = weight(_text_:searching in 5410) [ClassicSimilarity], result of:
          0.04673676 = score(doc=5410,freq=2.0), product of:
            0.2091384 = queryWeight, product of:
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.051699217 = queryNorm
            0.22347288 = fieldWeight in 5410, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.0452914 = idf(docFreq=2103, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5410)
      0.5 = coord(1/2)
  0.5 = coord(1/2)
```
Abstract

It is a frequently seen scenario that when people are not familiar with their search topics, they use a simple keyword search, which leads to a large amount of search results in multiple pages. This makes it difficult for users to pick relevant documents, especially given that they are not knowledgeable of the topics. To explore how systems can better help users find relevant documents from search results, the current research analyzed document selection behaviors of users with different levels of domain knowledge (DK). Data were collected in a laboratory study with 35 participants each searching on four tasks in the genomics domain. The results show that users with high and low DK levels selected different sets of documents to view; those high in DK read more documents and gave higher relevance ratings for the viewed documents than those low in DK did. Users with low DK tended to select documents ranking toward the top of the search result lists, and those with high in DK tended to also select documents ranking down the search result lists. The findings help design search systems that can personalize search results to users with different levels of DK.

Liu, J.: CIP in China : the development and status quo (1996) 0.01

0.010506795 = product of:
  0.02101359 = sum of:
    0.02101359 = product of:
      0.04202718 = sum of:
        0.04202718 = weight(_text_:22 in 5528) [ClassicSimilarity], result of:
          0.04202718 = score(doc=5528,freq=2.0), product of:
            0.18104185 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051699217 = queryNorm
            0.23214069 = fieldWeight in 5528, 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=5528)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Source: Cataloging and classification quarterly. 22(1996) no.1, S.69-76

Zhou, D.; Lawless, S.; Wu, X.; Zhao, W.; Liu, J.: ¬A study of user profile representation for personalized cross-language information retrieval (2016) 0.01

0.008755663 = product of:
  0.017511327 = sum of:
    0.017511327 = product of:
      0.035022654 = sum of:
        0.035022654 = weight(_text_:22 in 3167) [ClassicSimilarity], result of:
          0.035022654 = score(doc=3167,freq=2.0), product of:
            0.18104185 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051699217 = queryNorm
            0.19345059 = fieldWeight in 3167, 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=3167)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date: 20. 1.2015 18:30:22

Zhang, Y.; Liu, J.; Song, S.: ¬The design and evaluation of a nudge-based interface to facilitate consumers' evaluation of online health information credibility (2023) 0.01

0.008755663 = product of:
  0.017511327 = sum of:
    0.017511327 = product of:
      0.035022654 = sum of:
        0.035022654 = weight(_text_:22 in 993) [ClassicSimilarity], result of:
          0.035022654 = score(doc=993,freq=2.0), product of:
            0.18104185 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051699217 = queryNorm
            0.19345059 = fieldWeight in 993, 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=993)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date: 22. 6.2023 18:18:34

Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.01

0.008755663 = product of:
  0.017511327 = sum of:
    0.017511327 = product of:
      0.035022654 = sum of:
        0.035022654 = weight(_text_:22 in 1012) [ClassicSimilarity], result of:
          0.035022654 = score(doc=1012,freq=2.0), product of:
            0.18104185 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.051699217 = queryNorm
            0.19345059 = fieldWeight in 1012, 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=1012)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date: 22. 6.2023 14:55:20

Search (7 results, page 1 of 1)

Authors

Years

Themes