[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-293337-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-293337-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","group-recommender-systems-for-tourism-personality-and-group-preferences","Group recommender systems for tourism - Personality and Group Preferences","","Leisure travel is inherently emotional, so richer knowledge about tourists enables more personalized recommendations for attractions and travel-related concerns. Recommender systems for groups are harder due to cold-start effects and conflicting preferences among heterogeneous members. This study analyzes how Big Five personality traits predict choices of tourist attractions, travel motivations, and travel concerns in Portuguese participants (n=1035) using Exploratory and Confirmatory Factor Analysis, showing five traits inform attraction choices while only neuroticism and openness predict motivations.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/group-recommender-systems-for-tourism-personality-and-group-preferences/293337/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/group-recommender-systems-for-tourism-personality-and-group-preferences/293337.png","ImageObject",300,407,{"name":42,"@type":43},"Paura","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",13,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why is personalized recommendation for groups more difficult than for individuals?","Question",{"text":63,"@type":64},"Group recommendation is complicated by cold-start problems and by members having heterogeneous traits and conflicting preferences, which makes consistent group travel planning harder.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which personality traits predict tourists’ choices of attractions and preferences?",{"text":68,"@type":64},"The study finds all five Big Five personality dimensions can help predict the choice of tourist attractions and travel-related preferences and concerns.",{"name":70,"@type":61,"acceptedAnswer":71},"Which traits specifically predict travel motivations?",{"text":72,"@type":64},"Only neuroticism and openness predict traveling motivations, distinguishing motivation prediction from attraction and concern prediction.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},293337,1789662487,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":94,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144,"preview_page_count":4},13056712833777,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Group recommender systems for tourism: how does personality predict preferences for attractions, travel motivations, preferences and concerns?  \nPatrícia Alves1,2 · Helena Martins3 · Pedro Saraiva4 · João Carneiro2 · Paulo Novais1 · Goreti Marreiros2  \nReceived: 15 June 2022 / Accepted in revised form: 17 March 2023 © The Author(s) 2023  \nAbstract  \nTo travel in leisure is an emotional experience, and therefore, the more the information about the tourist is known, the more the personalized recommendations of places and attractions can be made. But if to provide recommendations to a tourist is complex, to provide them to a group is even more. The emergence of personality computing and personality-aware recommender systems (RS) brought a new solution for the coldstart problem inherent to the conventional RS and can be the leverage needed to solve conflicting preferences in heterogenous groups and to make more precise and personalized recommendations to tourists, as it has been evidenced that personality is strongly related to preferences in many domains, including tourism. Although many studies on psychology of tourism can be found, not many predict the tourists’ preferences based on the Big Five personality dimensions. This work aims to find how personality relates  \nB Patrícia Alves [pat@isep.ipp.pt](pat@isep.ipp.pt)  \nHelena Martins  \n[helenagmartins@gmail.com](helenagmartins@gmail.com)  \nPedro Saraiva  \n[pedro.sousa.saraiva@gmail.com](pedro.sousa.saraiva@gmail.com)  \nJoão Carneiro  \n[jrc@isep.ipp.pt](jrc@isep.ipp.pt)  \nPaulo Novais  \n[pjon@di.uminho.pt](pjon@di.uminho.pt)  \nGoreti Marreiros  \n[mgt@isep.ipp.pt](mgt@isep.ipp.pt)  \n1 ALGORITMI Research Centre/LASI, University of Minho, Guimarães, Portugal  \n2 GECAD/LASI, ISEP, Polytechnic of Porto, Porto, Portugal  \n3 CEOS.PP, ISCAP, Polytechnic of Porto, Porto, Portugal  \n4 Faculty of Psychology and Education Sciences of the University of Porto, Porto, Portugal  \n1 3  \nto the choice of a wide range of tourist attractions, traveling motivations, and travelrelated preferences and concerns, hoping to provide a solid base for researchers in the tourism RS area to automatically model tourists in the system without the need for tedious configurations, and solve the cold-start problem and conflicting preferences. By performing Exploratory and Confirmatory Factor Analysis on the data gathered from an online questionnaire, sent to Portuguese individuals from different areas of formation and age groups(n = 1035), we show allfive personality dimensions can help predict the choice of tourist attractions and travel-related preferences and concerns, and that only neuroticism and openness predict traveling motivations.  \nKeywords Group recommender systems · Personality · Tourist preferences · Travel motivations · Travel concerns · Affective computing  \n1 Introduction  \nThe last two decades have shown that personalization is the key to deliver the best recommendations in Recommender Systems (RS) (Chen et al. 2016; Poon and Huang 2017; Tkalcic and Chen 2015; Tkalcic et al. 2016; Tondello et al. 2017) . Therefore, the more about the user is known the more accurate and tailored recommendations can be made. But if to provide tailored individual recommendations is complex, to provide them to groups is even more(Masthoff2015). The tourism industry has many variables making it a very complex topic, which is aggravated when groups of tourists, that need a travel plan and to get accompanied in their excursions, are involved, particularly due to the group’s heterogeneity and conflicting preferences (Nguyen and Ricci 2018), making Group Recommender Systems (GRS) an important and challenging area of RS(Abolghasemi et al. 2022a, b;Castro et al.2015;Delicand Masthoff2018;Masthoff 2011; McCarthy et al. 2006) .  \nThe advancesin mobile technologies, like smartphones and wearable devices, make it possible to collect users’ detailed data (Adomavicius and Tuzhilin 2011), such as contextual information, identity, prefer","cbCaifmfTnSMoK25","https://ap.wps.com/l/cbCaifmfTnSMoK25","pdf",3741718,"English","# Abstract\n# Keywords\n# 1 Introduction","[{\"question\":\"Why is personalized recommendation for groups more difficult than for individuals?\",\"answer\":\"Group recommendation is complicated by cold-start problems and by members having heterogeneous traits and conflicting preferences, which makes consistent group travel planning harder.\"},{\"question\":\"Which personality traits predict tourists’ choices of attractions and preferences?\",\"answer\":\"The study finds all five Big Five personality dimensions can help predict the choice of tourist attractions and travel-related preferences and concerns.\"},{\"question\":\"Which traits specifically predict travel motivations?\",\"answer\":\"Only neuroticism and openness predict traveling motivations, distinguishing motivation prediction from attraction and concern prediction.\"}]","Group recommender systems for tourism - Personality and Group Preferences | PDF",176]