Visual E-Commerce Values Filtering Framework with Spatial Database metric

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10416983" target="_blank" >RIV/00216208:11320/20:10416983 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=N_4CwJlgGT" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=N_4CwJlgGT</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2298/CSIS200115030K" target="_blank" >10.2298/CSIS200115030K</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Visual E-Commerce Values Filtering Framework with Spatial Database metric

Popis výsledku v původním jazyce

Our customer preference model is based on aggregation of partly linear relaxations of value filters often used in e-commerce applications. Relaxation is motivated by the Analytic Hierarchy Processing method and combining fuzzy information in web accessible databases. In low dimensions our method is well suited also for data visualization. The process of translating models (user behavior) to programs (learned recommendation) is formalized by Challenge-Response Framework ChRF. ChRF resembles remote process call and reduction in combinatorial search problems. In our case, the model is automatically translated to a program using spatial database features. This enables us to define new metrics with visual motivation. We extend the conference paper with inductive ChRF, new representation of user and an additional method and metric. We provide experiments with synthetic data (items) and users.

Název v anglickém jazyce

Visual E-Commerce Values Filtering Framework with Spatial Database metric

Popis výsledku anglicky

Our customer preference model is based on aggregation of partly linear relaxations of value filters often used in e-commerce applications. Relaxation is motivated by the Analytic Hierarchy Processing method and combining fuzzy information in web accessible databases. In low dimensions our method is well suited also for data visualization. The process of translating models (user behavior) to programs (learned recommendation) is formalized by Challenge-Response Framework ChRF. ChRF resembles remote process call and reduction in combinatorial search problems. In our case, the model is automatically translated to a program using spatial database features. This enables us to define new metrics with visual motivation. We extend the conference paper with inductive ChRF, new representation of user and an additional method and metric. We provide experiments with synthetic data (items) and users.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Computer Science and Information Systems

ISSN

1820-0214

e-ISSN

—

Svazek periodika

17

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

RS - Srbská republika

Počet stran výsledku

24

Strana od-do

983-1006

Kód UT WoS článku

000580043200018

EID výsledku v databázi Scopus

2-s2.0-85093096389