Search (3 results, page 1 of 1)

0.0031955543 = product of:
  0.028759988 = sum of:
    0.028759988 = weight(_text_:data in 5356) [ClassicSimilarity], result of:
      0.028759988 = score(doc=5356,freq=4.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.24703519 = fieldWeight in 5356, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5356)
  0.11111111 = coord(1/9)

Abstract

The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first-of-its-kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology-based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).

0.003130991 = product of:
  0.02817892 = sum of:
    0.02817892 = weight(_text_:data in 5097) [ClassicSimilarity], result of:
      0.02817892 = score(doc=5097,freq=6.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.24204408 = fieldWeight in 5097, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.03125 = fieldNorm(doc=5097)
  0.11111111 = coord(1/9)

Abstract

Previous research investigated how to leverage the new type of social data available on the web, e.g., tags, ratings and reviews, in recommending and personalizing information. However, previous works mainly focused on predicting ratings using collaborative filtering or quantifying personalized ranking quality in simulations. As a consequence, the effect of social information in user's information search and information-selection behavior remains elusive. The objective of our research is to investigate the effects of social information on users' interactive search and information-selection behavior. We present a computational method and a system implementation combining different graph overlays: social, personal and search-time user input that are visualized for the user to support interactive information search. We report on a controlled laboratory experiment, in which 24 users performed search tasks using three system variants with different graphs as overlays composed from the largest publicly available social content and review data from Yelp: personal preferences, tags combined with personal preferences, and tags and social ratings combined with personal preferences. Data comprising search logs, questionnaires, simulations, and eye-tracking recordings show that: 1) the search effectiveness is improved by using and visualizing the social rating information and the personal preference information as compared to content-based ranking. 2) The need to consult external information before selecting information is reduced by the presentation of the effects of different overlays on the search results. Search effectiveness improvements can be attributed to the use of social rating and personal preference overlays, which was also confirmed in a follow-up simulation study. With the proposed method we demonstrate that social information can be incorporated to the interactive search process by overlaying graphs representing different information sources. We show that the combination of social rating information and personal preference information improves search effectiveness and reduce the need to consult external information. Our method and findings can inform the design of interactive search systems that leverage the information available on the social web.

0.0022595983 = product of:
  0.020336384 = sum of:
    0.020336384 = weight(_text_:data in 5915) [ClassicSimilarity], result of:
      0.020336384 = score(doc=5915,freq=2.0), product of:
        0.11642061 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.036818076 = queryNorm
        0.17468026 = fieldWeight in 5915, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5915)
  0.11111111 = coord(1/9)

Abstract

The outcome of exploratory information retrieval is not only dependent on the effectiveness of individual responses to a set of queries, but also on relevant information retrieved during the entire exploratory search session. We study the effect of search assistance, operationalized as an interactive faceted query suggestion, for both whole-session effectiveness and engagement through interactive faceted query suggestion. A user experiment is reported, where users performed exploratory search tasks, comparing interactive faceted query suggestion and a control condition with only conventional typed-query interaction. Data comprised of interaction and search logs show that the availability of interactive faceted query suggestion substantially improves whole-session effectiveness by increasing recall without sacrificing precision. The increased engagement with interactive faceted query suggestion is targeted to direct situated navigation around the initial query scope, but is not found to improve individual queries on average. The results imply that research in exploratory search should focus on measuring and designing tools that engage users with directed situated navigation support for improving whole-session performance.