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A minimalist survey collected 1,020 entries covering genre preferences, enabling inferences for personalized content matching via machine-learning techniques.",{"@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 & 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core idea of the proposed recommendation engine?","Question",{"text":63,"@type":64},"The engine recommends books, web media, music, and movies by mapping user MBTI personality types to media genre preferences derived from collected survey data.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What data sources and scale were used for training?",{"text":68,"@type":64},"An online survey hosted on personalitycafe.com and typologycentral.com, along with Facebook groups and reddit.com, produced 1,020 entries with ratings across 121+ objects after four months.",{"name":70,"@type":61,"acceptedAnswer":71},"How is clustering performed to analyze personality-type preferences?",{"text":72,"@type":64},"K-means clustering is applied after PCA (Principal Component Analysis) on each media category, producing 16 clusters corresponding to how many personality types prefer different content 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\nAbstract: Myers-Briggs Type Indicator (MBTI) types depict the psychological preferences by which a person perceives the world and make decisions. There are 4 principal functions through which the people see the world: sensation, intuition, feeling, and thinking. These functions along with the Introverted\\Extroverted nature of the person, there are 16 personalities types, the humans are divided into. Here an idea is presented where a user can get recommendations for books, web media content, music and movies on the basis of the users' MBTI type. Only things like books and other media content has been chosen because the preferences in such things are mostly subjective. Apart from the recommended content that is generally generated on the basis of the previous purchases, searches can be enhanced by using the MBTI. A minimalist survey was designed for collecting the data. This has a more than 100 features that show the preference of a personality type. Those include preferences in book genres, music genres, movie genres and even video games genres. After analyzing the data that is collected from the survey, some inferences were drawn from it which can be used to design a new recommendation engine for recommending the content that coincides with the personality of the user.  \nKeywords: Data Mining, Machine Learning, Clustering, Recommendation systems, Information retrieval, Big Data, MBTI.  \n1 Introduction  \nThe World Wide Web (WWW) is a great source of information, which if used intelligently can help the users as well as the businesses. Till now the WWW has undergone many developments. Currently, the applications are running on the Web 2.0 framework and slowly are transitioning to Web 3.0 which is the Semantic Web. Semantic web is the type of WWW from which the information can be yielded using the information present on the web i.e. using the metadata. With this metadata, personalization is done. The world of personalized media continues to enhance itself day to day. Mostly all search engines Google, Bing etc. try to orient results to reflect our social preferences and our past affinities. Facebook generates ads on the basis of your 'liked' pages, or your visits to certain pages. It also prioritizes newsfeed content based on our past engagement. Some major media channels like The Huffington Post or The New York Times all provide recommended content for users.  \nAccording to Econsultancy Report [1] released in June, 2012, majority (52%) of digital marketers say that personalization is fundamental to their online strategy. This raises a question that \"How can personalization affect our experiences as consumers and strategies as marketers?” Generally a more personalized web where irrelevant content can be ignored and relevant content can be presented to the user, is welcomed. Changes in customer preference can quickly be recognized by the marketer and the business strategy can be tailored specifically for that customer.  \nThere are a total of 16 Personality types in the Myers-Briggs Type Indicator (MBTI) . Each perceives an object differently. If a photograph is shown to a particular type and it has something that his thinking coincides with, then, it will appeal to him before any other object in the photograph. Same can be thought  \n*Bachelor of Technology, Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India  \nEmail ID: [apanimesh061@gmail.com](apanimesh061@gmail.com)  \nof media content. For example, According to websites like “[personalitycafe.com](personalitycafe.com)”,“[typologycentral.com](typologycentral.com)”  \nor “[personalitypage.com](personalitypage.com)”, INFPs (Introverted Intuitive Feeling Perceiving) might have a propensity towards religious texts or philosophy. Now, if on the internet a person of such type sees a few books where one of the books are of the above mentioned genre,","cbCaithmn24z2sLR","https://ap.wps.com/l/cbCaithmn24z2sLR","pdf",1250090,"English","# Introduction\n# Data Sets used\n# Clustering of Data","[{\"question\":\"What is the core idea of the proposed recommendation engine?\",\"answer\":\"The engine recommends books, web media, music, and movies by mapping user MBTI personality types to media genre preferences derived from collected survey data.\"},{\"question\":\"What data sources and scale were used for training?\",\"answer\":\"An online survey hosted on personalitycafe.com and typologycentral.com, along with Facebook groups and reddit.com, produced 1,020 entries with ratings across 121+ objects after four months.\"},{\"question\":\"How is clustering performed to analyze personality-type preferences?\",\"answer\":\"K-means clustering is applied after PCA (Principal Component Analysis) on each media category, producing 16 clusters corresponding to how many personality types prefer different content types.\"}]","Idea of a new Personality-Type based Recommendation Engine - Research Paper | PDF",18]