Degree of Similarity of Root Trees

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00322782" target="_blank" >RIV/68407700:21230/19:00322782 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-981-13-1056-0_57" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-13-1056-0_57</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-13-1056-0_57" target="_blank" >10.1007/978-981-13-1056-0_57</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Degree of Similarity of Root Trees

Popis výsledku v původním jazyce

Adaptive User Interfaces (UI) provide better user experience as users a receive personalized presentation. These UIs heavily rely on contextual data. Context helps the application to recognize user needs and thus adjust the UI. First time user receives a generalized experience; however, as the user uses the application more often it gathers lots of contextual data, such as the history of actions. This allows to statistically classify user in a user cluster and based on that adapt the UI presentation. This paper considers methods to find a measure of similarity of graphs to support adaptive UIs. To achieve this, it considers rooted trees. It states known approaches, which could be used for calculation of this measure. It focuses on the Simhash algorithm and describes its implementation in the SimCom experimental comparative application. Its results show that Simhash can be used for comparing the rooted trees. The main aim of this paper is to show novel view on how to use graph algorithms and clustering of trees into adaptive application structure.

Název v anglickém jazyce

Degree of Similarity of Root Trees

Popis výsledku anglicky

Adaptive User Interfaces (UI) provide better user experience as users a receive personalized presentation. These UIs heavily rely on contextual data. Context helps the application to recognize user needs and thus adjust the UI. First time user receives a generalized experience; however, as the user uses the application more often it gathers lots of contextual data, such as the history of actions. This allows to statistically classify user in a user cluster and based on that adapt the UI presentation. This paper considers methods to find a measure of similarity of graphs to support adaptive UIs. To achieve this, it considers rooted trees. It states known approaches, which could be used for calculation of this measure. It focuses on the Simhash algorithm and describes its implementation in the SimCom experimental comparative application. Its results show that Simhash can be used for comparing the rooted trees. The main aim of this paper is to show novel view on how to use graph algorithms and clustering of trees into adaptive application structure.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Electrical Engineering

ISBN

9789811310553

ISSN

1876-1100

e-ISSN

—

Počet stran výsledku

11

Strana od-do

581-591

Název nakladatele

Springer Nature Singapore Pte Ltd.

Místo vydání

—

Místo konání akce

Kawloon

Datum konání akce

25. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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