The influence of knowledge in the design of a recommender system to facilitate industrial symbiosis markets

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4 Citations (Scopus)

Abstract

Industrial symbiosis aims to stimulate or enhance cooperation between industrial firms to utilize industrial waste streams from other industries and to share related knowledge, in order to achieve sustainable production. Recommenders can support industries through the identification of item opportunities in waste marketplaces, enhancing activities that may lead to the development of an active waste exchange network. To build effective recommendation, we study the role of knowledge in the design of a recommender that suggests waste materials to be used in process industries. This paper compares the performance of a knowledge based input-output recommender with a recommender based on association rules. The two recommenders are evaluated with real-world data collected through deploying surveys in a workshop setting. Our research shows that many data challenges arise when creating recommendations from explicit knowledge and suggests that techniques based on the concept of implicit knowledge may be preferable in the design of an industrial symbiosis recommender.
Original languageEnglish
Pages (from-to)139-152
Number of pages14
JournalEnvironmental modelling & software
Volume110
DOIs
Publication statusPublished - Dec 2018

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Recommender systems
symbiosis
market
industry
Industry
Industrial wastes
Association rules
industrial waste
recommendation

Keywords

  • Industrial symbiosis
  • Recommender systems
  • Decision support systems
  • Input-output matching
  • Association-rule mining

Cite this

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title = "The influence of knowledge in the design of a recommender system to facilitate industrial symbiosis markets",
abstract = "Industrial symbiosis aims to stimulate or enhance cooperation between industrial firms to utilize industrial waste streams from other industries and to share related knowledge, in order to achieve sustainable production. Recommenders can support industries through the identification of item opportunities in waste marketplaces, enhancing activities that may lead to the development of an active waste exchange network. To build effective recommendation, we study the role of knowledge in the design of a recommender that suggests waste materials to be used in process industries. This paper compares the performance of a knowledge based input-output recommender with a recommender based on association rules. The two recommenders are evaluated with real-world data collected through deploying surveys in a workshop setting. Our research shows that many data challenges arise when creating recommendations from explicit knowledge and suggests that techniques based on the concept of implicit knowledge may be preferable in the design of an industrial symbiosis recommender.",
keywords = "Industrial symbiosis, Recommender systems, Decision support systems, Input-output matching, Association-rule mining",
author = "{van Capelleveen}, Guido and Chintan Amrit and Yazan, {Devrim Murat} and Henk Zijm",
year = "2018",
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doi = "10.1016/j.envsoft.2018.04.004",
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issn = "1364-8152",
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AU - van Capelleveen, Guido

AU - Amrit, Chintan

AU - Yazan, Devrim Murat

AU - Zijm, Henk

PY - 2018/12

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AB - Industrial symbiosis aims to stimulate or enhance cooperation between industrial firms to utilize industrial waste streams from other industries and to share related knowledge, in order to achieve sustainable production. Recommenders can support industries through the identification of item opportunities in waste marketplaces, enhancing activities that may lead to the development of an active waste exchange network. To build effective recommendation, we study the role of knowledge in the design of a recommender that suggests waste materials to be used in process industries. This paper compares the performance of a knowledge based input-output recommender with a recommender based on association rules. The two recommenders are evaluated with real-world data collected through deploying surveys in a workshop setting. Our research shows that many data challenges arise when creating recommendations from explicit knowledge and suggests that techniques based on the concept of implicit knowledge may be preferable in the design of an industrial symbiosis recommender.

KW - Industrial symbiosis

KW - Recommender systems

KW - Decision support systems

KW - Input-output matching

KW - Association-rule mining

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DO - 10.1016/j.envsoft.2018.04.004

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