Burkhardt, Dirk ; Pattan, Sachin ; Nazemi, Kawa ; Kuijper, Arjan (2017)
Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications.
In: Procedia Computer Science, 104
doi: 10.1016/j.procs.2017.01.170
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
A new approach for classifying users' search intentions is described in this paper. The approach uses the parameters: word frequency, query length and entity matching for distinguishing the user's query into exploratory, targeted and analysis search. The approach focuses mainly on word frequency analysis, where different sources for word frequency data are considered such as the Wortschatz frequency service by the University of Leipzig and the Microsoft Ngram service (now part of the Microsoft Cognitive Services). The model is evaluated with the help of a survey tool and few machine learning techniques. The survey was conducted with more than one hundred users and on evaluating the model with the collected data, the results are satisfactory. In big data applications the search intention analysis can be used to identify the purpose of a performed search, to provide an optimal initially set of visualizations that respects the intended task of the user to work with the result data.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Burkhardt, Dirk ; Pattan, Sachin ; Nazemi, Kawa ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia Computer Science |
| Jahrgang/Volume einer Zeitschrift: | 104 |
| DOI: | 10.1016/j.procs.2017.01.170 |
| URL / URN: | https://doi.org/10.1016/j.procs.2017.01.170 |
| Kurzbeschreibung (Abstract): | A new approach for classifying users' search intentions is described in this paper. The approach uses the parameters: word frequency, query length and entity matching for distinguishing the user's query into exploratory, targeted and analysis search. The approach focuses mainly on word frequency analysis, where different sources for word frequency data are considered such as the Wortschatz frequency service by the University of Leipzig and the Microsoft Ngram service (now part of the Microsoft Cognitive Services). The model is evaluated with the help of a survey tool and few machine learning techniques. The survey was conducted with more than one hundred users and on evaluating the model with the collected data, the results are satisfactory. In big data applications the search intention analysis can be used to identify the purpose of a performed search, to provide an optimal initially set of visualizations that respects the intended task of the user to work with the result data. |
| Freie Schlagworte: | Human-computer interaction (HCI), Information retrieval, User-centered design, Predictions |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 04 Mai 2020 12:51 |
| Letzte Änderung: | 04 Mai 2020 12:51 |
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