PriceCop – Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform

In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of information engineering and electronic business 2021-02, Vol.13 (1), p.1-14
Hauptverfasser: Zaim Shahrel, Mohamed, Mutalib, Sofianita, Abdul-Rahman, Shuzlina
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 14
container_issue 1
container_start_page 1
container_title International journal of information engineering and electronic business
container_volume 13
creator Zaim Shahrel, Mohamed
Mutalib, Sofianita
Abdul-Rahman, Shuzlina
description In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few irresponsible online retailers misled customers by hiking product prices before and during the sale, then applying huge discounts. Unfortunately, the “discounted” prices were found to be similar or only slightly lower than standard pricing. This problem occurs because users were unable to monitor product pricing due to time restrictions. This study proposes a Web application named PriceCop to help customers’ monitor product pricing. PriceCop is a significant application because it offers price prediction features to help users analyse product pricing within the next day; thus, it can help users to plan before making purchases. The price prediction model is developed by using Linear Regression (LR) technique. LR is commonly used to determine outcomes and used as predictors. Least Squares Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) are used as a comparison to evaluate the accuracy of the LR technique. LSSVM-ABC was initially proposed for stock market price predictions. The results show the accuracy of pricing prediction using LSSVM-ABC is 84%, while it is 62% when LR is employed. ABC is integrated into SVM to optimize the solution and is responsible for the best solution in every iteration. Even though LSSVM-ABC predicts product pricing more accurately than LR, this technique is best trained using at least a year’s worth of product prices, and the data is limited for this purpose. In the future, the dataset can be collected daily and trained for accuracy.
doi_str_mv 10.5815/ijieeb.2021.01.01
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2798556986</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2798556986</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1611-b012851e1d44a97e8a670de51d471dc6be4b4f6a656aa7651c745e6971a21c913</originalsourceid><addsrcrecordid>eNo9UMtOwzAQjBBIVKUfwM0S5xSvE9vJsVTlIaWiAso1cpxNcdXExU4P3PgH_pAvwaGI1Ui7M5rdlSaKLoFOeQb82mwNYjVllMGUDjiJRozKNM5pAqf_M0vOo4n3WxpKMJlmdBR1K2c0zu2efH9-kV9ClrYzvXVEdXVQsDa6N7Yja2-6DSlMh8qRJ9w49H7QB1vx_LqMZzdzssT-zdaeNGF_EWvbtujCydVO9UFqL6KzRu08Tv76OFrfLl7m93HxePcwnxWxBgEQVxRYxgGhTlOVS8yUkLRGHriEWosK0ypthBJcKCUFBy1TjiKXoBjoHJJxdHW8u3f2_YC-L7f24LrwsmQyzzgXeSaCC44u7az3Dpty70yr3EcJtBySLY_JlkOyJR2Q_AAuEmyM</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2798556986</pqid></control><display><type>article</type><title>PriceCop – Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Zaim Shahrel, Mohamed ; Mutalib, Sofianita ; Abdul-Rahman, Shuzlina</creator><creatorcontrib>Zaim Shahrel, Mohamed ; Mutalib, Sofianita ; Abdul-Rahman, Shuzlina ; Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia</creatorcontrib><description>In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few irresponsible online retailers misled customers by hiking product prices before and during the sale, then applying huge discounts. Unfortunately, the “discounted” prices were found to be similar or only slightly lower than standard pricing. This problem occurs because users were unable to monitor product pricing due to time restrictions. This study proposes a Web application named PriceCop to help customers’ monitor product pricing. PriceCop is a significant application because it offers price prediction features to help users analyse product pricing within the next day; thus, it can help users to plan before making purchases. The price prediction model is developed by using Linear Regression (LR) technique. LR is commonly used to determine outcomes and used as predictors. Least Squares Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) are used as a comparison to evaluate the accuracy of the LR technique. LSSVM-ABC was initially proposed for stock market price predictions. The results show the accuracy of pricing prediction using LSSVM-ABC is 84%, while it is 62% when LR is employed. ABC is integrated into SVM to optimize the solution and is responsible for the best solution in every iteration. Even though LSSVM-ABC predicts product pricing more accurately than LR, this technique is best trained using at least a year’s worth of product prices, and the data is limited for this purpose. In the future, the dataset can be collected daily and trained for accuracy.</description><identifier>ISSN: 2074-9023</identifier><identifier>EISSN: 2074-9031</identifier><identifier>DOI: 10.5815/ijieeb.2021.01.01</identifier><language>eng</language><publisher>Hong Kong: Modern Education and Computer Science Press</publisher><subject>Accuracy ; Applications programs ; Customers ; Electronic commerce ; Iterative methods ; Prediction models ; Prices ; Pricing ; Regression analysis ; Support vector machines ; Swarm intelligence</subject><ispartof>International journal of information engineering and electronic business, 2021-02, Vol.13 (1), p.1-14</ispartof><rights>2021. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at http://www.mecs-press.org/ijcnis/terms.html</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1611-b012851e1d44a97e8a670de51d471dc6be4b4f6a656aa7651c745e6971a21c913</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,27929,27930</link.rule.ids></links><search><creatorcontrib>Zaim Shahrel, Mohamed</creatorcontrib><creatorcontrib>Mutalib, Sofianita</creatorcontrib><creatorcontrib>Abdul-Rahman, Shuzlina</creatorcontrib><creatorcontrib>Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia</creatorcontrib><title>PriceCop – Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform</title><title>International journal of information engineering and electronic business</title><description>In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few irresponsible online retailers misled customers by hiking product prices before and during the sale, then applying huge discounts. Unfortunately, the “discounted” prices were found to be similar or only slightly lower than standard pricing. This problem occurs because users were unable to monitor product pricing due to time restrictions. This study proposes a Web application named PriceCop to help customers’ monitor product pricing. PriceCop is a significant application because it offers price prediction features to help users analyse product pricing within the next day; thus, it can help users to plan before making purchases. The price prediction model is developed by using Linear Regression (LR) technique. LR is commonly used to determine outcomes and used as predictors. Least Squares Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) are used as a comparison to evaluate the accuracy of the LR technique. LSSVM-ABC was initially proposed for stock market price predictions. The results show the accuracy of pricing prediction using LSSVM-ABC is 84%, while it is 62% when LR is employed. ABC is integrated into SVM to optimize the solution and is responsible for the best solution in every iteration. Even though LSSVM-ABC predicts product pricing more accurately than LR, this technique is best trained using at least a year’s worth of product prices, and the data is limited for this purpose. In the future, the dataset can be collected daily and trained for accuracy.</description><subject>Accuracy</subject><subject>Applications programs</subject><subject>Customers</subject><subject>Electronic commerce</subject><subject>Iterative methods</subject><subject>Prediction models</subject><subject>Prices</subject><subject>Pricing</subject><subject>Regression analysis</subject><subject>Support vector machines</subject><subject>Swarm intelligence</subject><issn>2074-9023</issn><issn>2074-9031</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNo9UMtOwzAQjBBIVKUfwM0S5xSvE9vJsVTlIaWiAso1cpxNcdXExU4P3PgH_pAvwaGI1Ui7M5rdlSaKLoFOeQb82mwNYjVllMGUDjiJRozKNM5pAqf_M0vOo4n3WxpKMJlmdBR1K2c0zu2efH9-kV9ClrYzvXVEdXVQsDa6N7Yja2-6DSlMh8qRJ9w49H7QB1vx_LqMZzdzssT-zdaeNGF_EWvbtujCydVO9UFqL6KzRu08Tv76OFrfLl7m93HxePcwnxWxBgEQVxRYxgGhTlOVS8yUkLRGHriEWosK0ypthBJcKCUFBy1TjiKXoBjoHJJxdHW8u3f2_YC-L7f24LrwsmQyzzgXeSaCC44u7az3Dpty70yr3EcJtBySLY_JlkOyJR2Q_AAuEmyM</recordid><startdate>20210208</startdate><enddate>20210208</enddate><creator>Zaim Shahrel, Mohamed</creator><creator>Mutalib, Sofianita</creator><creator>Abdul-Rahman, Shuzlina</creator><general>Modern Education and Computer Science Press</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>8AL</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BVBZV</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20210208</creationdate><title>PriceCop – Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform</title><author>Zaim Shahrel, Mohamed ; Mutalib, Sofianita ; Abdul-Rahman, Shuzlina</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1611-b012851e1d44a97e8a670de51d471dc6be4b4f6a656aa7651c745e6971a21c913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Applications programs</topic><topic>Customers</topic><topic>Electronic commerce</topic><topic>Iterative methods</topic><topic>Prediction models</topic><topic>Prices</topic><topic>Pricing</topic><topic>Regression analysis</topic><topic>Support vector machines</topic><topic>Swarm intelligence</topic><toplevel>online_resources</toplevel><creatorcontrib>Zaim Shahrel, Mohamed</creatorcontrib><creatorcontrib>Mutalib, Sofianita</creatorcontrib><creatorcontrib>Abdul-Rahman, Shuzlina</creatorcontrib><creatorcontrib>Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Proquest Central</collection><collection>Technology Collection</collection><collection>East &amp; South Asia Database</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Computing Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>International journal of information engineering and electronic business</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zaim Shahrel, Mohamed</au><au>Mutalib, Sofianita</au><au>Abdul-Rahman, Shuzlina</au><aucorp>Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>PriceCop – Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform</atitle><jtitle>International journal of information engineering and electronic business</jtitle><date>2021-02-08</date><risdate>2021</risdate><volume>13</volume><issue>1</issue><spage>1</spage><epage>14</epage><pages>1-14</pages><issn>2074-9023</issn><eissn>2074-9031</eissn><abstract>In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few irresponsible online retailers misled customers by hiking product prices before and during the sale, then applying huge discounts. Unfortunately, the “discounted” prices were found to be similar or only slightly lower than standard pricing. This problem occurs because users were unable to monitor product pricing due to time restrictions. This study proposes a Web application named PriceCop to help customers’ monitor product pricing. PriceCop is a significant application because it offers price prediction features to help users analyse product pricing within the next day; thus, it can help users to plan before making purchases. The price prediction model is developed by using Linear Regression (LR) technique. LR is commonly used to determine outcomes and used as predictors. Least Squares Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) are used as a comparison to evaluate the accuracy of the LR technique. LSSVM-ABC was initially proposed for stock market price predictions. The results show the accuracy of pricing prediction using LSSVM-ABC is 84%, while it is 62% when LR is employed. ABC is integrated into SVM to optimize the solution and is responsible for the best solution in every iteration. Even though LSSVM-ABC predicts product pricing more accurately than LR, this technique is best trained using at least a year’s worth of product prices, and the data is limited for this purpose. In the future, the dataset can be collected daily and trained for accuracy.</abstract><cop>Hong Kong</cop><pub>Modern Education and Computer Science Press</pub><doi>10.5815/ijieeb.2021.01.01</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2074-9023
ispartof International journal of information engineering and electronic business, 2021-02, Vol.13 (1), p.1-14
issn 2074-9023
2074-9031
language eng
recordid cdi_proquest_journals_2798556986
source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Accuracy
Applications programs
Customers
Electronic commerce
Iterative methods
Prediction models
Prices
Pricing
Regression analysis
Support vector machines
Swarm intelligence
title PriceCop – Price Monitor and Prediction Using Linear Regression and LSVM-ABC Methods for E-commerce Platform
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-14T23%3A26%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=PriceCop%20%E2%80%93%20Price%20Monitor%20and%20Prediction%20Using%20Linear%20Regression%20and%20LSVM-ABC%20Methods%20for%20E-commerce%20Platform&rft.jtitle=International%20journal%20of%20information%20engineering%20and%20electronic%20business&rft.au=Zaim%20Shahrel,%20Mohamed&rft.aucorp=Faculty%20of%20Computer%20and%20Mathematical%20Sciences,%20Universiti%20Teknologi%20MARA,%2040450%20Shah%20Alam,%20Selangor,%20Malaysia&rft.date=2021-02-08&rft.volume=13&rft.issue=1&rft.spage=1&rft.epage=14&rft.pages=1-14&rft.issn=2074-9023&rft.eissn=2074-9031&rft_id=info:doi/10.5815/ijieeb.2021.01.01&rft_dat=%3Cproquest_cross%3E2798556986%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2798556986&rft_id=info:pmid/&rfr_iscdi=true