Search (2 results, page 1 of 1)

  • × theme_ss:"Suchmaschinen"
  • × theme_ss:"Suchtaktik"
  • × year_i:[2020 TO 2030}
  1. Kang, X.; Wu, Y.; Ren, W.: Toward action comprehension for searching : mining actionable intents in query entities (2020) 0.00
    0.0018318077 = product of:
      0.0036636153 = sum of:
        0.0036636153 = product of:
          0.0073272306 = sum of:
            0.0073272306 = weight(_text_:a in 5613) [ClassicSimilarity], result of:
              0.0073272306 = score(doc=5613,freq=14.0), product of:
                0.043477926 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.037706986 = queryNorm
                0.1685276 = fieldWeight in 5613, product of:
                  3.7416575 = tf(freq=14.0), with freq of:
                    14.0 = termFreq=14.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5613)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    Understanding search engine users' intents has been a popular study in information retrieval, which directly affects the quality of retrieved information. One of the fundamental problems in this field is to find a connection between the entity in a query and the potential intents of the users, the latter of which would further reveal important information for facilitating the users' future actions. In this article, we present a novel research method for mining the actionable intents for search users, by generating a ranked list of the potentially most informative actions based on a massive pool of action samples. We compare different search strategies and their combinations for retrieving the action pool and develop three criteria for measuring the informativeness of the selected action samples, that is, the significance of an action sample within the pool, the representativeness of an action sample for the other candidate samples, and the diverseness of an action sample with respect to the selected actions. Our experiment, based on the Action Mining (AM) query entity data set from the Actionable Knowledge Graph (AKG) task at NTCIR-13, suggests that the proposed approach is effective in generating an informative and early-satisfying ranking of potential actions for search users.
    Type
    a
  2. Sa, N.; Yuan, X.(J.): Improving the effectiveness of voice search systems through partial query modification (2022) 0.00
    0.0016616598 = product of:
      0.0033233196 = sum of:
        0.0033233196 = product of:
          0.006646639 = sum of:
            0.006646639 = weight(_text_:a in 635) [ClassicSimilarity], result of:
              0.006646639 = score(doc=635,freq=8.0), product of:
                0.043477926 = queryWeight, product of:
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.037706986 = queryNorm
                0.15287387 = fieldWeight in 635, product of:
                  2.828427 = tf(freq=8.0), with freq of:
                    8.0 = termFreq=8.0
                  1.153047 = idf(docFreq=37942, maxDocs=44218)
                  0.046875 = fieldNorm(doc=635)
          0.5 = coord(1/2)
      0.5 = coord(1/2)
    
    Abstract
    This paper addresses the importance of improving the effectiveness of voice search systems through partial query modification. A user-centered experiment was designed to compare the effectiveness of an experimental system using partial query modification feature to a baseline system in which users could issue complete queries only, with 32 participants each searching on eight different tasks. The results indicate that the participants spent significantly more time preparing the modification but significantly less time speaking the modification by using the experimental system than by using the baseline system. The participants found that the experimental system (a) was more effective, (b) gave them more control, (c) was easier for the search tasks, and (d) saved them time than the baseline system. The results contribute to improving future voice search system design and benefiting the research community in general. System implications and future work were discussed.
    Type
    a