All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

ProMap: Product Mapping Datasets

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10484938" target="_blank" >RIV/00216208:11320/24:10484938 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1007/978-3-031-56060-6_11" target="_blank" >https://doi.org/10.1007/978-3-031-56060-6_11</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-031-56060-6_11" target="_blank" >10.1007/978-3-031-56060-6_11</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    ProMap: Product Mapping Datasets

  • Original language description

    The goal of product mapping is to decide, whether two listings from two different e-shops describe the same products. Existing datasets of matching and non-matching pairs of products, however, often suffer from incomplete product information or contain only very distant non-matching products. In this paper, we introduce two new datasets for product mapping: ProMapCz consisting of 1,495 Czech product pairs and ProMapEn consisting of 1,555 English product pairs of matching and non-matching products manually scraped from two pairs of e-shops. The datasets contain both images and textual descriptions of the products, including their specifications, making them one of the most complete datasets for product mapping. Additionally, we divide the non-matching products into two different categories - close non-matches and medium non-matches, based on how similar the products are to each other. Even the medium non-matches are, however, pairs of products that are much more similar than non-matches in other datasets - for example, they still need to have the same brand and similar name and price. Finally, we train a number of product matching models on these datasets to demonstrate the advantages of having these two types of non-matches for the analysis of these models.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2024

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    ADVANCES IN INFORMATION RETRIEVAL, ECIR 2024, PT II

  • ISBN

    978-3-031-56059-0

  • ISSN

    0302-9743

  • e-ISSN

    1611-3349

  • Number of pages

    14

  • Pages from-to

    159-172

  • Publisher name

    SPRINGER INTERNATIONAL PUBLISHING AG

  • Place of publication

    CHAM

  • Event location

    Glasgow

  • Event date

    Mar 24, 2024

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article

    001211832000011