A master thesis from Aalborg University
Generic detection of user interaction behavior
[Generisk detektering af brugerinteraktionsmønstre]
Author(s)
Term
4. term
Education
Publication year
2011
Submitted on
2012-01-23
Pages
125 pages
Abstract
Stigende mængder af data, komplekse og relationsbasereded datastrukturer og individuelt tilpasset indhold udfordrer de aktuelle repræsentationer af data, fortrinsvis begrænset til en liste-baseret tilgang, som ikke beskriver forholdet mellem data objekter. Da datastrukturer ændrer sig i retning af semantisk markup, bliver forholdet mellem data objekter værdifulde og kan angives som "meta-affordances", som brugeren kan anvende til at forstå forholdet mellem data. Adaptive systemer, der bygger på Bruger Modeller har tidligere været baseret på brugerens historik eller modeller af det komplette miljø. Åbne miljøer, såsom internettet, kræver en anden tilgang og dette projekt giver en metode til generisk detektere og vurdere forskelle i brugernes adfærd, der kunne forfine eksisterende brugermodeller. Data fra en simpel visuel indekseringsopgave, som brugeren løser i et sæt af varierende miljøer, der fungerer som grundlag for at kortlægge brugerens adfærd.Resultatet er, at påvisning af brugernes adfærd ved brug af simple generiske kalibrationsrutiner er muligt i visuelle interfaces - metoden kan også anvendes i HCI i almindelighed.
ncreasing data spaces, relation-based data structures, and individual customized data is challenging the current data representations, mainly limited to a list-based approach, which does not describe the relationships between data objects. When data structures are changing towards semantic markup, the relations between data objects become valuable and can be stated as "meta-affordances", which the user can utilize in understanding relationships between data. Adaptive systems that relies on User Models have previously been based on user history or models of the complete environment. Open environments, such as the Internet, requires a different approach & this project provides a method to generically evaluate differences in user behavior that could refine existing User Models. Data from a simple visual indexation tasks that the user solves in a set of different environments, serves as a basis for detecting user behavior. The results are that detection of user behavior using simple generic calibration is possible in visual interfaces - the method could also be applied in HCI in general which will be encouraged by the authors.
Keywords
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