Model Prediction of the Next Runway Model using Decision Tree and Random Forest (Case Study: Big Four Fashion Week)

Fashion is a multi-billion-dollar industry with social and economic implication worldwide. Fashion industry values three trillion US dollars with 4% of global market share worlwide, where retail value of luxury goods market worth 339.4 billion dollars and the value of the womenswear industry worth 521 billion dollars. Fashion Week is one of the most important events within fashion industry. Fashion Week as an essential show aims to introduce new trends and collections on moving bodies, presenting how the cut and fabric interplayed with the body. A moving body represents by fashion models, a special talent in fashion who displays the attractiveness of the brand in advertisements and runways. Fashion models is discovered through agency who also distribute them to the network of fashion world. Modeling agency is the curators that discover beauty to define aesthetic of agency. Modeling agencies are always looking for new talents, also called scouting. The growing popularity of social media and online technology has opened a new way for talents to self-promote and agents to scout for new talents. Technological changes provide several opportunities. Recently, model agencies outsource talent scouting and other related services that cost 25.3% of revenue. Many fields of study have been shifting to digitalization, including fashion industry. One of key pillars in fashion industry 4.0 is the emerging of artificial intelligence, allowing us to imitate the knowledge of traditional talent scouting process into an automation model based on machine learning practice. The primary objective of this study is to build an automation model of talent scouting process using application of Machine Learning for Classification task. Specifically, the paper consider Decision Tree, and Random Forest to predict early success of fashion model under the merging framework of science of success

A Model for Predicting Music Popularity on Streaming Platforms

The global music market moves billions of dollars every year, most of which comes from streamingplatforms. In this paper, we present a model for predicting whether or not a song will appear in Spotify’s Top 50, a ranking of the 50 most popular songs in Spotify, which is one of today’s biggest streaming services. To make this prediction, we trained different classifiers with information from audio features from songs that appeared in this ranking between November 2018 and January 2019. When tested with data from June and July 2019, an SVM classifier with RBF kernel obtained accuracy, precision, and AUC above 80%

Advertising Cross-Platform: Promotion Methods for Independent Musicians in the Era of Live Streaming

A study of promotion techniques for independent musicians in the 21st century, after the covid-19 pandemic and the rise of live streaming. This paper looks at previous research on live streaming and promotion methods in the music industry, and combines that data with a survey of the public\u27s perception of local music events. The results of this survey were used to promote and conduct a music concert that was viewable both in person and over live stream

2018 SDSU Data Science Symposium Program

Table of Contents: Letter from SDSU PresidentLetter from SDSU Department of Mathematics and Statistics Dept. HeadSponsorsGeneral InformationKeynote SpeakersInvited SpeakersSunday ScheduleWorkshop InformationMonday ScheduleAbstracts| Invited SpeakersAbstracts | Oral PresentationsPoster PresentationCommittee and Volunteer

A manifesto for the creative economy

The UK\u27s creative economy is one of its great national strengths, historically deeply rooted and accounting for around one-tenth of the whole economy. It provides jobs for 2.5 million people – more than in financial services, advanced manufacturing or construction – and in recent years, this creative workforce has grown four times faster than the workforce as a whole. But behind this success lies much disruption and business uncertainty, associated with digital technologies. Previously profitable business models have been swept away, young companies from outside the UK have dominated new internet markets, and some UK creative businesses have struggled to compete. UK policymakers too have failed to keep pace with developments in North America and parts of Asia. But it is not too late to refresh tired policies. This manifesto sets out our 10-point plan to bolster one of the UK\u27s fastest growing sectors

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality

Measuring Collective Attention in Online Content: Sampling, Engagement, and Network Effects

The production and consumption of online content have been increasing rapidly, whereas human attention is a scarce resource. Understanding how the content captures collective attention has become a challenge of growing importance. In this thesis, we tackle this challenge from three fronts -- quantifying sampling effects of social media data; measuring engagement behaviors towards online content; and estimating network effects induced by the recommender systems. Data sampling is a fundamental problem. To obtain a list of items, one common method is sampling based on the item prevalence in social media streams. However, social data is often noisy and incomplete, which may affect the subsequent observations. For each item, user behaviors can be conceptualized as two steps -- the first step is relevant to the content appeal, measured by the number of clicks; the second step is relevant to the content quality, measured by the post-clicking metrics, e.g., dwell time, likes, or comments. We categorize online attention (behaviors) into two classes: popularity (clicking) and engagement (watching, liking, or commenting). Moreover, modern platforms use recommender systems to present the users with a tailoring content display for maximizing satisfaction. The recommendation alters the appeal of an item by changing its ranking, and consequently impacts its popularity. Our research is enabled by the data available from the largest video hosting site YouTube. We use YouTube URLs shared on Twitter as a sampling protocol to obtain a collection of videos, and we track their prevalence from 2015 to 2019. This method creates a longitudinal dataset consisting of more than 5 billion tweets. Albeit the volume is substantial, we find Twitter still subsamples the data. Our dataset covers about 80% of all tweets with YouTube URLs. We present a comprehensive measurement study of the Twitter sampling effects across different timescales and different subjects. We find that the volume of missing tweets can be estimated by Twitter rate limit messages, true entity ranking can be inferred based on sampled observations, and sampling compromises the quality of network and diffusion models. Next, we present the first large-scale measurement study of how users collectively engage with YouTube videos. We study the time and percentage of each video being watched. We propose a duration-calibrated metric, called relative engagement, which is correlated with recognized notion of content quality, stable over time, and predictable even before a video's upload. Lastly, we examine the network effects induced by the YouTube recommender system. We construct the recommendation network for 60,740 music videos from 4,435 professional artists. An edge indicates that the target video is recommended on the webpage of source video. We discover the popularity bias -- videos are disproportionately recommended towards more popular videos. We use the bow-tie structure to characterize the network and find that the largest strongly connected component consists of 23.1% of videos while occupying 82.6% of attention. We also build models to estimate the latent influence between videos and artists. By taking into account the network structure, we can predict video popularity 9.7% better than other baselines. Altogether, we explore the collective consuming patterns of human attention towards online content. Methods and findings from this thesis can be used by content producers, hosting sites, and online users alike to improve content production, advertising strategies, and recommender systems. We expect our new metrics, methods, and observations can generalize to other multimedia platforms such as the music streaming service Spotify

DU Undergraduate Showcase: Research, Scholarship, and Creative Works: Abstracts

Abstracts from the DU Undergraduate Showcase