My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles

New mapping and location applications focus on offering improved usability and services based on multi-modal door to door passenger experiences. This helps citizens develop greater confidence in and adherence to multi-modal transport services. These applications adapt to the needs of the user during...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Electronics (Basel) 2021-06, Vol.10 (11), p.1263
Hauptverfasser: Rivas, Alberto, González-Briones, Alfonso, Cea-Morán, Juan J., Prat-Pérez, Arnau, Corchado, Juan M.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 11
container_start_page 1263
container_title Electronics (Basel)
container_volume 10
creator Rivas, Alberto
González-Briones, Alfonso
Cea-Morán, Juan J.
Prat-Pérez, Arnau
Corchado, Juan M.
description New mapping and location applications focus on offering improved usability and services based on multi-modal door to door passenger experiences. This helps citizens develop greater confidence in and adherence to multi-modal transport services. These applications adapt to the needs of the user during their journey through the data, statistics and trends extracted from their previous uses of the application. The My-Trac application is dedicated to the research and development of these user-centered services to improve the multi-modal experience using various techniques. Among these techniques are preference extraction systems, which extract user information from social networks, such as Twitter. In this article, we present a system that allows to develop a profile of the preferences of each user, on the basis of the tweets published on their Twitter account. The system extracts the tweets from the profile and analyzes them using the proposed algorithms and returns the result in a document containing the categories and the degree of affinity that the user has with each category. In this way, the My-Trac application includes a recommender system where the user receives preference-based suggestions about activities or services on the route to be taken.
doi_str_mv 10.3390/electronics10111263
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2539622635</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2539622635</sourcerecordid><originalsourceid>FETCH-LOGICAL-c322t-de32c8caffd16dca690492c6e58be1d6a8e928fe3ad44715a6b63a298678ce303</originalsourceid><addsrcrecordid>eNptkE9LAzEQxYMoWGo_gZeA59Uks5sm3rT4p1CxaD14WtLsBLd0NzVJkX57U-vBg3OZB7_HzOMRcs7ZJYBmV7hGm4LvWxs545wLCUdkINhYF1pocfxHn5JRjCuWR3NQwAbk_WlXLIKx1_R1FxN21PlAX9D6rsO-Man1PfWOzn3bp7hX0z5hwJhoBukD6a2J7Q9YfLUpIzoP3rVrjGfkxJl1xNHvHpK3-7vF5LGYPT9MJzezwoIQqWgQhFXWONdw2VgjNSu1sBIrtUTeSKNQC-UQTFOWY14ZuZRghFZyrCwCgyG5ONzdBP-5zcnqld-GPr-sRQVaitxHlV1wcNngYwzo6k1oOxN2NWf1vsb6nxrhGwceaQQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2539622635</pqid></control><display><type>article</type><title>My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Rivas, Alberto ; González-Briones, Alfonso ; Cea-Morán, Juan J. ; Prat-Pérez, Arnau ; Corchado, Juan M.</creator><creatorcontrib>Rivas, Alberto ; González-Briones, Alfonso ; Cea-Morán, Juan J. ; Prat-Pérez, Arnau ; Corchado, Juan M.</creatorcontrib><description>New mapping and location applications focus on offering improved usability and services based on multi-modal door to door passenger experiences. This helps citizens develop greater confidence in and adherence to multi-modal transport services. These applications adapt to the needs of the user during their journey through the data, statistics and trends extracted from their previous uses of the application. The My-Trac application is dedicated to the research and development of these user-centered services to improve the multi-modal experience using various techniques. Among these techniques are preference extraction systems, which extract user information from social networks, such as Twitter. In this article, we present a system that allows to develop a profile of the preferences of each user, on the basis of the tweets published on their Twitter account. The system extracts the tweets from the profile and analyzes them using the proposed algorithms and returns the result in a document containing the categories and the degree of affinity that the user has with each category. In this way, the My-Trac application includes a recommender system where the user receives preference-based suggestions about activities or services on the route to be taken.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics10111263</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Advertising ; Algorithms ; Communication ; Data mining ; Internet ; Public transportation ; R&amp;D ; Recommender systems ; Research &amp; development ; Social networks ; Society</subject><ispartof>Electronics (Basel), 2021-06, Vol.10 (11), p.1263</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c322t-de32c8caffd16dca690492c6e58be1d6a8e928fe3ad44715a6b63a298678ce303</citedby><cites>FETCH-LOGICAL-c322t-de32c8caffd16dca690492c6e58be1d6a8e928fe3ad44715a6b63a298678ce303</cites><orcidid>0000-0002-9558-9895 ; 0000-0002-2829-1829 ; 0000-0002-3444-4393</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Rivas, Alberto</creatorcontrib><creatorcontrib>González-Briones, Alfonso</creatorcontrib><creatorcontrib>Cea-Morán, Juan J.</creatorcontrib><creatorcontrib>Prat-Pérez, Arnau</creatorcontrib><creatorcontrib>Corchado, Juan M.</creatorcontrib><title>My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles</title><title>Electronics (Basel)</title><description>New mapping and location applications focus on offering improved usability and services based on multi-modal door to door passenger experiences. This helps citizens develop greater confidence in and adherence to multi-modal transport services. These applications adapt to the needs of the user during their journey through the data, statistics and trends extracted from their previous uses of the application. The My-Trac application is dedicated to the research and development of these user-centered services to improve the multi-modal experience using various techniques. Among these techniques are preference extraction systems, which extract user information from social networks, such as Twitter. In this article, we present a system that allows to develop a profile of the preferences of each user, on the basis of the tweets published on their Twitter account. The system extracts the tweets from the profile and analyzes them using the proposed algorithms and returns the result in a document containing the categories and the degree of affinity that the user has with each category. In this way, the My-Trac application includes a recommender system where the user receives preference-based suggestions about activities or services on the route to be taken.</description><subject>Advertising</subject><subject>Algorithms</subject><subject>Communication</subject><subject>Data mining</subject><subject>Internet</subject><subject>Public transportation</subject><subject>R&amp;D</subject><subject>Recommender systems</subject><subject>Research &amp; development</subject><subject>Social networks</subject><subject>Society</subject><issn>2079-9292</issn><issn>2079-9292</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><recordid>eNptkE9LAzEQxYMoWGo_gZeA59Uks5sm3rT4p1CxaD14WtLsBLd0NzVJkX57U-vBg3OZB7_HzOMRcs7ZJYBmV7hGm4LvWxs545wLCUdkINhYF1pocfxHn5JRjCuWR3NQwAbk_WlXLIKx1_R1FxN21PlAX9D6rsO-Man1PfWOzn3bp7hX0z5hwJhoBukD6a2J7Q9YfLUpIzoP3rVrjGfkxJl1xNHvHpK3-7vF5LGYPT9MJzezwoIQqWgQhFXWONdw2VgjNSu1sBIrtUTeSKNQC-UQTFOWY14ZuZRghFZyrCwCgyG5ONzdBP-5zcnqld-GPr-sRQVaitxHlV1wcNngYwzo6k1oOxN2NWf1vsb6nxrhGwceaQQ</recordid><startdate>20210601</startdate><enddate>20210601</enddate><creator>Rivas, Alberto</creator><creator>González-Briones, Alfonso</creator><creator>Cea-Morán, Juan J.</creator><creator>Prat-Pérez, Arnau</creator><creator>Corchado, Juan M.</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-9558-9895</orcidid><orcidid>https://orcid.org/0000-0002-2829-1829</orcidid><orcidid>https://orcid.org/0000-0002-3444-4393</orcidid></search><sort><creationdate>20210601</creationdate><title>My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles</title><author>Rivas, Alberto ; González-Briones, Alfonso ; Cea-Morán, Juan J. ; Prat-Pérez, Arnau ; Corchado, Juan M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c322t-de32c8caffd16dca690492c6e58be1d6a8e928fe3ad44715a6b63a298678ce303</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Advertising</topic><topic>Algorithms</topic><topic>Communication</topic><topic>Data mining</topic><topic>Internet</topic><topic>Public transportation</topic><topic>R&amp;D</topic><topic>Recommender systems</topic><topic>Research &amp; development</topic><topic>Social networks</topic><topic>Society</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rivas, Alberto</creatorcontrib><creatorcontrib>González-Briones, Alfonso</creatorcontrib><creatorcontrib>Cea-Morán, Juan J.</creatorcontrib><creatorcontrib>Prat-Pérez, Arnau</creatorcontrib><creatorcontrib>Corchado, Juan M.</creatorcontrib><collection>CrossRef</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Publicly Available Content Database</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><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rivas, Alberto</au><au>González-Briones, Alfonso</au><au>Cea-Morán, Juan J.</au><au>Prat-Pérez, Arnau</au><au>Corchado, Juan M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles</atitle><jtitle>Electronics (Basel)</jtitle><date>2021-06-01</date><risdate>2021</risdate><volume>10</volume><issue>11</issue><spage>1263</spage><pages>1263-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>New mapping and location applications focus on offering improved usability and services based on multi-modal door to door passenger experiences. This helps citizens develop greater confidence in and adherence to multi-modal transport services. These applications adapt to the needs of the user during their journey through the data, statistics and trends extracted from their previous uses of the application. The My-Trac application is dedicated to the research and development of these user-centered services to improve the multi-modal experience using various techniques. Among these techniques are preference extraction systems, which extract user information from social networks, such as Twitter. In this article, we present a system that allows to develop a profile of the preferences of each user, on the basis of the tweets published on their Twitter account. The system extracts the tweets from the profile and analyzes them using the proposed algorithms and returns the result in a document containing the categories and the degree of affinity that the user has with each category. In this way, the My-Trac application includes a recommender system where the user receives preference-based suggestions about activities or services on the route to be taken.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/electronics10111263</doi><orcidid>https://orcid.org/0000-0002-9558-9895</orcidid><orcidid>https://orcid.org/0000-0002-2829-1829</orcidid><orcidid>https://orcid.org/0000-0002-3444-4393</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2079-9292
ispartof Electronics (Basel), 2021-06, Vol.10 (11), p.1263
issn 2079-9292
2079-9292
language eng
recordid cdi_proquest_journals_2539622635
source MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals
subjects Advertising
Algorithms
Communication
Data mining
Internet
Public transportation
R&D
Recommender systems
Research & development
Social networks
Society
title My-Trac: System for Recommendation of Points of Interest on the Basis of Twitter Profiles
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T21%3A24%3A48IST&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=My-Trac:%20System%20for%20Recommendation%20of%20Points%20of%20Interest%20on%20the%20Basis%20of%20Twitter%20Profiles&rft.jtitle=Electronics%20(Basel)&rft.au=Rivas,%20Alberto&rft.date=2021-06-01&rft.volume=10&rft.issue=11&rft.spage=1263&rft.pages=1263-&rft.issn=2079-9292&rft.eissn=2079-9292&rft_id=info:doi/10.3390/electronics10111263&rft_dat=%3Cproquest_cross%3E2539622635%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=2539622635&rft_id=info:pmid/&rfr_iscdi=true