Search (2 results, page 1 of 1)

0.015969018 = product of:
  0.06387607 = sum of:
    0.06387607 = weight(_text_:engines in 1081) [ClassicSimilarity], result of:
      0.06387607 = score(doc=1081,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.2806784 = fieldWeight in 1081, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1081)
  0.25 = coord(1/4)

Abstract

The aim of the work described in this paper is to evaluate the influencing effects of query-biased summaries in web searching. For this purpose, a summarisation system has been developed, and a summary tailored to the user's query is generated automatically for each document retrieved. The system aims to provide both a better means of assessing document relevance than titles or abstracts typical of many web search result lists. Through visiting each result page at retrieval-time, the system provides the user with an idea of the current page content and thus deals with the dynamic nature of the web. To examine the effectiveness of this approach, a task-oriented, comparative evaluation between four different web retrieval systems was performed; two that use query-biased summarisation, and two that use the standard ranked titles/abstracts approach. The results from the evaluation indicate that query-biased summarisation techniques appear to be more useful and effective in helping users gauge document relevance than the traditional ranked titles/abstracts approach. The same methodology was used to compare the effectiveness of two of the web's major search engines; AltaVista and Google.

0.015969018 = product of:
  0.06387607 = sum of:
    0.06387607 = weight(_text_:engines in 0) [ClassicSimilarity], result of:
      0.06387607 = score(doc=0,freq=2.0), product of:
        0.22757743 = queryWeight, product of:
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.04479146 = queryNorm
        0.2806784 = fieldWeight in 0, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          5.080822 = idf(docFreq=746, maxDocs=44218)
          0.0390625 = fieldNorm(doc=0)
  0.25 = coord(1/4)

Abstract

As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model supported by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world.